diff --git a/0.15.7/examples/example-1-floquet/index.html b/0.15.7/examples/example-1-floquet/index.html
index 4cbc144..165c2d7 100644
--- a/0.15.7/examples/example-1-floquet/index.html
+++ b/0.15.7/examples/example-1-floquet/index.html
@@ -386,14 +386,14 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=8693e1e2"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-1-floquet.py> Download Script </a> <a class=md-button download href=../../assets/data/floquet-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download _ Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=44b409ec"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=single-qubit-floquet-dynamics>Single Qubit Floquet Dynamics<a class=anchor-link href=#single-qubit-floquet-dynamics>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>Rounding out the single qubit examples we will show how to generate a Floquet protocol. We will define the probotol using a python function and then use the Bloqade API to sample the function at certain intervals to make it compatible with the hardware, which only supports piecewise linear/constant functions. First let us start with the imports.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9d4bd960"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=483e282f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-1-floquet.py> Download Script </a> <a class=md-button download href=../../assets/data/floquet-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download _ Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6804bf5e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=single-qubit-floquet-dynamics>Single Qubit Floquet Dynamics<a class=anchor-link href=#single-qubit-floquet-dynamics>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>Rounding out the single qubit examples we will show how to generate a Floquet protocol. We will define the probotol using a python function and then use the Bloqade API to sample the function at certain intervals to make it compatible with the hardware, which only supports piecewise linear/constant functions. First let us start with the imports.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=56e21f4b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>import</span> <span class=nn>os</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
 <span class=kn>import</span> <span class=nn>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>):</span>
     <span class=n>os</span><span class=o>.</span><span class=n>mkdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, save, load import os import numpy as np import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=091ac40f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>For the floquet protocol we keep We do the same Rabi drive but allow the detuning to vary sinusoidally. We do this by defining a smooth function for the detuning and then sampling it at certain intervals (in this case, the minimum hardware-supported time step). Note that the <code>sample</code> method will always sample at equal to or greater than the specified time step. If the total time interval is not divisible by the time step, the last time step will be larger than the specified time step. Also note that the arguments of your function must be named arguments, e.g. no <code>*args</code> or <code>**kwargs</code>, because Bloqade will analyze the function signature to and generate variables for each argument.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=6267413b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>min_time_step</span> <span class=o>=</span> <span class=mf>0.05</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, save, load import os import numpy as np import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3a583377"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>For the floquet protocol we keep We do the same Rabi drive but allow the detuning to vary sinusoidally. We do this by defining a smooth function for the detuning and then sampling it at certain intervals (in this case, the minimum hardware-supported time step). Note that the <code>sample</code> method will always sample at equal to or greater than the specified time step. If the total time interval is not divisible by the time step, the last time step will be larger than the specified time step. Also note that the arguments of your function must be named arguments, e.g. no <code>*args</code> or <code>**kwargs</code>, because Bloqade will analyze the function signature to and generate variables for each argument.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=7969eb23"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>min_time_step</span> <span class=o>=</span> <span class=mf>0.05</span>
 
 <span class=n>durations</span> <span class=o>=</span> <span class=n>cast</span><span class=p>([</span><span class=s2>"ramp_time"</span><span class=p>,</span> <span class=s2>"run_time"</span><span class=p>,</span> <span class=s2>"ramp_time"</span><span class=p>])</span>
 
@@ -410,7 +410,7 @@
     <span class=o>.</span><span class=n>detuning</span><span class=o>.</span><span class=n>uniform</span><span class=o>.</span><span class=n>fn</span><span class=p>(</span><span class=n>detuning_wf</span><span class=p>,</span> <span class=nb>sum</span><span class=p>(</span><span class=n>durations</span><span class=p>))</span>
     <span class=o>.</span><span class=n>sample</span><span class=p>(</span><span class=s2>"min_time_step"</span><span class=p>,</span> <span class=s2>"linear"</span><span class=p>)</span>
 <span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2> min_time_step = 0.05 durations = cast(["ramp_time", "run_time", "ramp_time"]) def detuning_wf(t, drive_amplitude, drive_frequency): return drive_amplitude * np.sin(drive_frequency * t) floquet_program = ( start.add_position((0, 0)) .rydberg.rabi.amplitude.uniform.piecewise_linear( durations, [0, "rabi_max", "rabi_max", 0] ) .detuning.uniform.fn(detuning_wf, sum(durations)) .sample("min_time_step", "linear") )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=255fd65a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>We assign values to the necessary variables and then run_async the program to both the emulator and actual hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=f8a055e5"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>run_times</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>linspace</span><span class=p>(</span><span class=mf>0.05</span><span class=p>,</span> <span class=mf>3.0</span><span class=p>,</span> <span class=mi>101</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2> min_time_step = 0.05 durations = cast(["ramp_time", "run_time", "ramp_time"]) def detuning_wf(t, drive_amplitude, drive_frequency): return drive_amplitude * np.sin(drive_frequency * t) floquet_program = ( start.add_position((0, 0)) .rydberg.rabi.amplitude.uniform.piecewise_linear( durations, [0, "rabi_max", "rabi_max", 0] ) .detuning.uniform.fn(detuning_wf, sum(durations)) .sample("min_time_step", "linear") )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0aa156fb"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>We assign values to the necessary variables and then run_async the program to both the emulator and actual hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=bac03267"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>run_times</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>linspace</span><span class=p>(</span><span class=mf>0.05</span><span class=p>,</span> <span class=mf>3.0</span><span class=p>,</span> <span class=mi>101</span><span class=p>)</span>
 
 <span class=n>floquet_job</span> <span class=o>=</span> <span class=n>floquet_program</span><span class=o>.</span><span class=n>assign</span><span class=p>(</span>
     <span class=n>ramp_time</span><span class=o>=</span><span class=mf>0.06</span><span class=p>,</span>
@@ -419,7 +419,7 @@
     <span class=n>drive_amplitude</span><span class=o>=</span><span class=mi>15</span><span class=p>,</span>
     <span class=n>drive_frequency</span><span class=o>=</span><span class=mi>15</span><span class=p>,</span>
 <span class=p>)</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>run_time</span><span class=o>=</span><span class=n>run_times</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3>run_times = np.linspace(0.05, 3.0, 101) floquet_job = floquet_program.assign( ramp_time=0.06, min_time_step=0.05, rabi_max=15, drive_amplitude=15, drive_frequency=15, ).batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=df51400f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>have to start the time at 0.05 because the hardware does not support anything less than that time step. We can now run_async the job to the emulator and hardware.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=76f39d03"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>Like in the first tutorial, we will run the program on the emulator and hardware. Note that for the hardware we will use the <code>parallelize</code> method to run multiple copies of the program in parallel. For more information about this process, see the first tutorial.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=53f1df19"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"floquet-emulation.json"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3>run_times = np.linspace(0.05, 3.0, 101) floquet_job = floquet_program.assign( ramp_time=0.06, min_time_step=0.05, rabi_max=15, drive_amplitude=15, drive_frequency=15, ).batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b599a970"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>have to start the time at 0.05 because the hardware does not support anything less than that time step. We can now run_async the job to the emulator and hardware.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=652ffc8b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>Like in the first tutorial, we will run the program on the emulator and hardware. Note that for the hardware we will use the <code>parallelize</code> method to run multiple copies of the program in parallel. For more information about this process, see the first tutorial.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9d5d5079"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"floquet-emulation.json"</span><span class=p>)</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>):</span>
     <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>floquet_job</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>local_emulator</span><span class=p>()</span><span class=o>.</span><span class=n>run</span><span class=p>(</span><span class=mi>10000</span><span class=p>)</span>
@@ -430,11 +430,11 @@
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>hardware_filename</span><span class=p>):</span>
     <span class=n>batch</span> <span class=o>=</span> <span class=n>floquet_job</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>50</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>batch</span><span class=p>,</span> <span class=n>hardware_filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4>emu_filename = os.path.join(os.path.abspath(""), "data", "floquet-emulation.json") if not os.path.isfile(emu_filename): emu_batch = floquet_job.braket.local_emulator().run(10000) save(emu_batch, emu_filename) hardware_filename = os.path.join(os.path.abspath(""), "data", "floquet-job.json") if not os.path.isfile(hardware_filename): batch = floquet_job.parallelize(24).braket.aquila().run_async(shots=50) save(batch, hardware_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=15097f40"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>Exactly like in the Rabi Oscillation example, we can now plot the results from the hardware and emulation together. Again we will use the <code>report</code> to calculate the mean Rydberg population for each run, and then plot the results.</p> <p>first we load the results from the emulation and hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a15e311e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4>emu_filename = os.path.join(os.path.abspath(""), "data", "floquet-emulation.json") if not os.path.isfile(emu_filename): emu_batch = floquet_job.braket.local_emulator().run(10000) save(emu_batch, emu_filename) hardware_filename = os.path.join(os.path.abspath(""), "data", "floquet-job.json") if not os.path.isfile(hardware_filename): batch = floquet_job.parallelize(24).braket.aquila().run_async(shots=50) save(batch, hardware_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=feb605a6"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>Exactly like in the Rabi Oscillation example, we can now plot the results from the hardware and emulation together. Again we will use the <code>report</code> to calculate the mean Rydberg population for each run, and then plot the results.</p> <p>first we load the results from the emulation and hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ce872584"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>hardware_filename</span><span class=p>)</span>
 <span class=c1># hardware_batch.fetch()</span>
 <span class=c1># save(filename, hardware_batch)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5>emu_batch = load(emu_filename) hardware_batch = load(hardware_filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0ebbbb29"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Next we extract the run times and the Rydberg population from the report. We can then plot the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=8ad1216a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5>emu_batch = load(emu_filename) hardware_batch = load(hardware_filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2ef7d77b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Next we extract the run times and the Rydberg population from the report. We can then plot the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=267d8d5e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>emulator_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 
 <span class=n>times</span> <span class=o>=</span> <span class=n>emulator_report</span><span class=o>.</span><span class=n>list_param</span><span class=p>(</span><span class=s2>"run_time"</span><span class=p>)</span>
@@ -449,4 +449,4 @@
 <span class=n>plt</span><span class=o>.</span><span class=n>ylabel</span><span class=p>(</span><span class=s2>"Rydberg population"</span><span class=p>)</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>legend</span><span class=p>()</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6> hardware_report = hardware_batch.report() emulator_report = emu_batch.report() times = emulator_report.list_param("run_time") density = [1 - ele.mean() for ele in emulator_report.bitstrings()] plt.plot(times, density, color="#878787", marker=".", label="Emulator") times = hardware_report.list_param("run_time") density = [1 - ele.mean() for ele in hardware_report.bitstrings()] plt.plot(times, density, color="#6437FF", linewidth=4, label="QPU") plt.xlabel("Time ($\mu s$)") plt.ylabel("Rydberg population") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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"> 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+</pre></div> <div class=clipboard-copy-txt id=cell-6> hardware_report = hardware_batch.report() emulator_report = emu_batch.report() times = emulator_report.list_param("run_time") density = [1 - ele.mean() for ele in emulator_report.bitstrings()] plt.plot(times, density, color="#878787", marker=".", label="Emulator") times = hardware_report.list_param("run_time") density = [1 - ele.mean() for ele in hardware_report.bitstrings()] plt.plot(times, density, color="#6437FF", linewidth=4, label="QPU") plt.xlabel("Time ($\mu s$)") plt.ylabel("Rydberg population") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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"> 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\ No newline at end of file
diff --git a/0.15.7/examples/example-1-rabi/index.html b/0.15.7/examples/example-1-rabi/index.html
index 9f42066..8f8d961 100644
--- a/0.15.7/examples/example-1-rabi/index.html
+++ b/0.15.7/examples/example-1-rabi/index.html
@@ -386,14 +386,14 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=162e6c4e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-1-rabi.py> Download Script </a> <a class=md-button download href=../../assets/data/rabi-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=03e07734"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=single-qubit-rabi-oscillations>Single Qubit Rabi Oscillations<a class=anchor-link href=#single-qubit-rabi-oscillations>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to use Bloqade to emulate Rabi oscillations of a Neutral Atom and run it on hardware. We will define a Rabi oscillation as a sequence with a constant detuning and Rabi frequency. In practice, the Rabi frequency has to start and end at 0.0, so we will use a piecewise linear function to ramp up and down the Rabi frequency.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=24129175"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>load</span><span class=p>,</span> <span class=n>save</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=75015280"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-1-rabi.py> Download Script </a> <a class=md-button download href=../../assets/data/rabi-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=718a3495"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=single-qubit-rabi-oscillations>Single Qubit Rabi Oscillations<a class=anchor-link href=#single-qubit-rabi-oscillations>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to use Bloqade to emulate Rabi oscillations of a Neutral Atom and run it on hardware. We will define a Rabi oscillation as a sequence with a constant detuning and Rabi frequency. In practice, the Rabi frequency has to start and end at 0.0, so we will use a piecewise linear function to ramp up and down the Rabi frequency.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=06b056eb"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>load</span><span class=p>,</span> <span class=n>save</span>
 <span class=kn>import</span> <span class=nn>os</span>
 <span class=kn>import</span> <span class=nn>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>):</span>
     <span class=n>os</span><span class=o>.</span><span class=n>mkdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, load, save import os import matplotlib.pyplot as plt import numpy as np if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=09878b18"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>Below we define program with one atom, with constant detuning but variable Rabi frequency, ramping up to "rabi_ampl" and then returning to 0.0. Note that the <code>cast</code> function can be used to create a variable that can used in multiple places in the program. These variables support basic arithmetic operations, such as addition, subtraction, multiplication, and division. They also have <code>min</code> and <code>max</code> methods that can be used in place of built-in python <code>min</code> and <code>max</code> functions, e.g. <code>cast("a").min(cast("b"))</code>.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a6752329"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>durations</span> <span class=o>=</span> <span class=n>cast</span><span class=p>([</span><span class=s2>"ramp_time"</span><span class=p>,</span> <span class=s2>"run_time"</span><span class=p>,</span> <span class=s2>"ramp_time"</span><span class=p>])</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, load, save import os import matplotlib.pyplot as plt import numpy as np if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=518888fd"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>Below we define program with one atom, with constant detuning but variable Rabi frequency, ramping up to "rabi_ampl" and then returning to 0.0. Note that the <code>cast</code> function can be used to create a variable that can used in multiple places in the program. These variables support basic arithmetic operations, such as addition, subtraction, multiplication, and division. They also have <code>min</code> and <code>max</code> methods that can be used in place of built-in python <code>min</code> and <code>max</code> functions, e.g. <code>cast("a").min(cast("b"))</code>.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=f6ebd204"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>durations</span> <span class=o>=</span> <span class=n>cast</span><span class=p>([</span><span class=s2>"ramp_time"</span><span class=p>,</span> <span class=s2>"run_time"</span><span class=p>,</span> <span class=s2>"ramp_time"</span><span class=p>])</span>
 
 <span class=n>rabi_oscillations_program</span> <span class=o>=</span> <span class=p>(</span>
     <span class=n>start</span><span class=o>.</span><span class=n>add_position</span><span class=p>((</span><span class=mi>0</span><span class=p>,</span> <span class=mi>0</span><span class=p>))</span>
@@ -402,26 +402,26 @@
     <span class=p>)</span>
     <span class=o>.</span><span class=n>detuning</span><span class=o>.</span><span class=n>uniform</span><span class=o>.</span><span class=n>constant</span><span class=p>(</span><span class=n>duration</span><span class=o>=</span><span class=nb>sum</span><span class=p>(</span><span class=n>durations</span><span class=p>),</span> <span class=n>value</span><span class=o>=</span><span class=s2>"detuning_value"</span><span class=p>)</span>
 <span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2>durations = cast(["ramp_time", "run_time", "ramp_time"]) rabi_oscillations_program = ( start.add_position((0, 0)) .rydberg.rabi.amplitude.uniform.piecewise_linear( durations=durations, values=[0, "rabi_ampl", "rabi_ampl", 0] ) .detuning.uniform.constant(duration=sum(durations), value="detuning_value") )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ce306362"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=assign-values-to-the-variables-in-the-program>Assign values to the variables in the program,<a class=anchor-link href=#assign-values-to-the-variables-in-the-program>¶</a></h2><p>Once your program is built, you can use the <code>assign</code> method to assign values to the variables in the program. These values must be numeric, and can be either <code>int</code>, <code>float</code>, or <code>Decimal</code> (from the <code>decimal</code> module). Note that the <code>Decimal</code> type is used to represent real numbers exactly, whereas <code>float</code> is a 64-bit floating point numberthat is only accurate to about 15 decimal places. The <code>Decimal</code> type is recommended for Bloqade programs, as it will ensure that your program is simulated and run with the highest possible precision. We can also do a parameter scan using the <code>batch_assign</code> method, which will create a different program for each value provided in the list. In this case, we are sweeping the <code>run_time</code> variable, which is the time that the Rabi amplitude stays at the value of <code>rabi_ampl</code>.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3b292fed"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>run_times</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>linspace</span><span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=mi>101</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2>durations = cast(["ramp_time", "run_time", "ramp_time"]) rabi_oscillations_program = ( start.add_position((0, 0)) .rydberg.rabi.amplitude.uniform.piecewise_linear( durations=durations, values=[0, "rabi_ampl", "rabi_ampl", 0] ) .detuning.uniform.constant(duration=sum(durations), value="detuning_value") )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=375e8308"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=assign-values-to-the-variables-in-the-program>Assign values to the variables in the program,<a class=anchor-link href=#assign-values-to-the-variables-in-the-program>¶</a></h2><p>Once your program is built, you can use the <code>assign</code> method to assign values to the variables in the program. These values must be numeric, and can be either <code>int</code>, <code>float</code>, or <code>Decimal</code> (from the <code>decimal</code> module). Note that the <code>Decimal</code> type is used to represent real numbers exactly, whereas <code>float</code> is a 64-bit floating point numberthat is only accurate to about 15 decimal places. The <code>Decimal</code> type is recommended for Bloqade programs, as it will ensure that your program is simulated and run with the highest possible precision. We can also do a parameter scan using the <code>batch_assign</code> method, which will create a different program for each value provided in the list. In this case, we are sweeping the <code>run_time</code> variable, which is the time that the Rabi amplitude stays at the value of <code>rabi_ampl</code>.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=7d5ace79"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>run_times</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>linspace</span><span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=mi>101</span><span class=p>)</span>
 
 <span class=n>rabi_oscillation_job</span> <span class=o>=</span> <span class=n>rabi_oscillations_program</span><span class=o>.</span><span class=n>assign</span><span class=p>(</span>
     <span class=n>ramp_time</span><span class=o>=</span><span class=mf>0.06</span><span class=p>,</span> <span class=n>rabi_ampl</span><span class=o>=</span><span class=mi>15</span><span class=p>,</span> <span class=n>detuning_value</span><span class=o>=</span><span class=mf>0.0</span>
 <span class=p>)</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>run_time</span><span class=o>=</span><span class=n>run_times</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3>run_times = np.linspace(0, 3, 101) rabi_oscillation_job = rabi_oscillations_program.assign( ramp_time=0.06, rabi_ampl=15, detuning_value=0.0 ).batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1928db93"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>To run the program on the emulator we can select the <code>braket</code> provider as a property of the <code>batch</code> object. Braket has its own emulator that we can use to run the program. To do this select <code>local_emulator</code> as the next option followed by the <code>run</code> method. Then we dump the results to a file so that we can use them later. Note that unline the actual hardware the shots do not correspond to multiple executions of the emuatlor, but rather the number of times the final wavefunction is sampled. This is because the emulator does not simulate any noise.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=987b61e5"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"rabi-emulation.json"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3>run_times = np.linspace(0, 3, 101) rabi_oscillation_job = rabi_oscillations_program.assign( ramp_time=0.06, rabi_ampl=15, detuning_value=0.0 ).batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f600d8d6"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>To run the program on the emulator we can select the <code>braket</code> provider as a property of the <code>batch</code> object. Braket has its own emulator that we can use to run the program. To do this select <code>local_emulator</code> as the next option followed by the <code>run</code> method. Then we dump the results to a file so that we can use them later. Note that unline the actual hardware the shots do not correspond to multiple executions of the emuatlor, but rather the number of times the final wavefunction is sampled. This is because the emulator does not simulate any noise.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a1270157"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"rabi-emulation.json"</span><span class=p>)</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>):</span>
     <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>rabi_oscillation_job</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>local_emulator</span><span class=p>()</span><span class=o>.</span><span class=n>run</span><span class=p>(</span><span class=mi>10000</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>emu_batch</span><span class=p>,</span> <span class=n>emu_filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4> emu_filename = os.path.join(os.path.abspath(""), "data", "rabi-emulation.json") if not os.path.isfile(emu_filename): emu_batch = rabi_oscillation_job.braket.local_emulator().run(10000) save(emu_batch, emu_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=12e3d63f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>When running on the hardware we can use the <code>braket</code> provider. However, we will need to specify the device to run on. In this case we will use <em>Aquila</em> via the <code>aquila</code> method. Before that we must note that because Aquila can support up to 256 atoms in an area that is $75 \times 76 \mu m^2$. We need to make full use of the capabilities of the device. Bloqade automatically takes care of this with the <code>parallelize</code> method, which will allow us to run multiple copies of the program in parallel using the full user provided area of Aquila. The <code>parallelize</code> method takes a single argument, which is the distance between each copy of the program on a grid. In this case, we want to make sure that the distance between each atom is at least 24 micrometers, so that the Rydberg interactions between atoms are negligible.</p> <p>To run the program but not wait for the results, we can use the <code>run_async</code> method, which will return a <code>Batch</code> object that can be used to fetch the results later. After running the program, we dump the results to a file so that we can use them later. Note that if you want to wait for the results in the python script just call the <code>run</code> method instead of <code>run_async</code>. This will block the script until all results in the batch are complete.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=25abf285"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"rabi-job.json"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4> emu_filename = os.path.join(os.path.abspath(""), "data", "rabi-emulation.json") if not os.path.isfile(emu_filename): emu_batch = rabi_oscillation_job.braket.local_emulator().run(10000) save(emu_batch, emu_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=5a6782ba"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>When running on the hardware we can use the <code>braket</code> provider. However, we will need to specify the device to run on. In this case we will use <em>Aquila</em> via the <code>aquila</code> method. Before that we must note that because Aquila can support up to 256 atoms in an area that is $75 \times 76 \mu m^2$. We need to make full use of the capabilities of the device. Bloqade automatically takes care of this with the <code>parallelize</code> method, which will allow us to run multiple copies of the program in parallel using the full user provided area of Aquila. The <code>parallelize</code> method takes a single argument, which is the distance between each copy of the program on a grid. In this case, we want to make sure that the distance between each atom is at least 24 micrometers, so that the Rydberg interactions between atoms are negligible.</p> <p>To run the program but not wait for the results, we can use the <code>run_async</code> method, which will return a <code>Batch</code> object that can be used to fetch the results later. After running the program, we dump the results to a file so that we can use them later. Note that if you want to wait for the results in the python script just call the <code>run</code> method instead of <code>run_async</code>. This will block the script until all results in the batch are complete.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a110ea21"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"rabi-job.json"</span><span class=p>)</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>hardware_filename</span><span class=p>):</span>
     <span class=n>batch</span> <span class=o>=</span> <span class=n>rabi_oscillation_job</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=mi>1000</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>batch</span><span class=p>,</span> <span class=n>hardware_filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5>hardware_filename = os.path.join(os.path.abspath(""), "data", "rabi-job.json") if not os.path.isfile(hardware_filename): batch = rabi_oscillation_job.parallelize(24).braket.aquila().run_async(1000) save(batch, hardware_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=5ca14219"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>The quantity of interest in this example is the probability of finding the atom in the Rydberg state, which is given by the <code>0</code> measurement outcome. The reason that <code>0</code> is the Rydberg state is because the in the actual device the Rydberg atoms are pushed out of the trap area and show up as a dark spot in the image. To get the probability of being in the Rydberg state, we can use the <code>bitstrings</code> method of the <code>Report</code> object, which returns a list of numpy arrays containing the measurement outcomes for each shot. We can then use the <code>mean</code> method of the numpy array to get the probability of being in the Rydberg state for each shot. We can then plot the results as a function of time. the time value can be obtained from the <code>run_time</code> parameter of the <code>Report</code> object as a list.</p> <p>before that we need to load the results from our previously saved files using the <code>load</code> function from Bloqade:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=2febbdbc"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5>hardware_filename = os.path.join(os.path.abspath(""), "data", "rabi-job.json") if not os.path.isfile(hardware_filename): batch = rabi_oscillation_job.parallelize(24).braket.aquila().run_async(1000) save(batch, hardware_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6ac92673"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>The quantity of interest in this example is the probability of finding the atom in the Rydberg state, which is given by the <code>0</code> measurement outcome. The reason that <code>0</code> is the Rydberg state is because the in the actual device the Rydberg atoms are pushed out of the trap area and show up as a dark spot in the image. To get the probability of being in the Rydberg state, we can use the <code>bitstrings</code> method of the <code>Report</code> object, which returns a list of numpy arrays containing the measurement outcomes for each shot. We can then use the <code>mean</code> method of the numpy array to get the probability of being in the Rydberg state for each shot. We can then plot the results as a function of time. the time value can be obtained from the <code>run_time</code> parameter of the <code>Report</code> object as a list.</p> <p>before that we need to load the results from our previously saved files using the <code>load</code> function from Bloqade:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a9bff8c2"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>hardware_filename</span><span class=p>)</span>
 <span class=c1># hardware_batch.fetch()</span>
 <span class=c1># save(filename, hardware_batch)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6>emu_batch = load(emu_filename) hardware_batch = load(hardware_filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=434178c8"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-6>emu_batch = load(emu_filename) hardware_batch = load(hardware_filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=35cbb44b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>emulator_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 
 <span class=n>times</span> <span class=o>=</span> <span class=n>emulator_report</span><span class=o>.</span><span class=n>list_param</span><span class=p>(</span><span class=s2>"run_time"</span><span class=p>)</span>
@@ -436,4 +436,4 @@
 <span class=n>plt</span><span class=o>.</span><span class=n>ylabel</span><span class=p>(</span><span class=s2>"Rydberg population"</span><span class=p>)</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>legend</span><span class=p>()</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-7>hardware_report = hardware_batch.report() emulator_report = emu_batch.report() times = emulator_report.list_param("run_time") density = [1 - ele.mean() for ele in emulator_report.bitstrings()] plt.plot(times, density, color="#878787", marker=".", label="Emulator") times = hardware_report.list_param("run_time") density = [1 - ele.mean() for ele in hardware_report.bitstrings()] plt.plot(times, density, color="#6437FF", linewidth=4, label="QPU") plt.xlabel("Time ($\mu s$)") plt.ylabel("Rydberg population") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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> 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+</pre></div> <div class=clipboard-copy-txt id=cell-7>hardware_report = hardware_batch.report() emulator_report = emu_batch.report() times = emulator_report.list_param("run_time") density = [1 - ele.mean() for ele in emulator_report.bitstrings()] plt.plot(times, density, color="#878787", marker=".", label="Emulator") times = hardware_report.list_param("run_time") density = [1 - ele.mean() for ele in hardware_report.bitstrings()] plt.plot(times, density, color="#6437FF", linewidth=4, label="QPU") plt.xlabel("Time ($\mu s$)") plt.ylabel("Rydberg population") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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> 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2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> </a> <a href=https://pypi.org/project/bloqade/ target=_blank rel=noopener title=pypi.org class=md-social__link> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 448 512"><!-- Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.8 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\ No newline at end of file
diff --git a/0.15.7/examples/example-1-ramsey/index.html b/0.15.7/examples/example-1-ramsey/index.html
index 648f1c8..eab6e38 100644
--- a/0.15.7/examples/example-1-ramsey/index.html
+++ b/0.15.7/examples/example-1-ramsey/index.html
@@ -386,7 +386,7 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9f6e083f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-1-ramsey.py> Download Script </a> <a class=md-button download href=../../assets/data/ramsey-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9287222a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=single-qubit-ramsey-protocol>Single Qubit Ramsey Protocol<a class=anchor-link href=#single-qubit-ramsey-protocol>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to use Bloqade to emulate a Ramsey protocol as well as run it on hardware. We will define a Ramsey protocol as a sequence of two $\pi/2$ pulses separated by a variable time gap $\tau$. These procols are used to measure the coherence time of a qubit. In practice, the Rabi frequency has to start and end at 0.0, so we will use a piecewise linear function to ramp up and down the Rabi frequency.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8032232e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ac25432d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-1-ramsey.py> Download Script </a> <a class=md-button download href=../../assets/data/ramsey-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=84a2e144"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=single-qubit-ramsey-protocol>Single Qubit Ramsey Protocol<a class=anchor-link href=#single-qubit-ramsey-protocol>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to use Bloqade to emulate a Ramsey protocol as well as run it on hardware. We will define a Ramsey protocol as a sequence of two $\pi/2$ pulses separated by a variable time gap $\tau$. These procols are used to measure the coherence time of a qubit. In practice, the Rabi frequency has to start and end at 0.0, so we will use a piecewise linear function to ramp up and down the Rabi frequency.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=2477ab53"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>from</span> <span class=nn>decimal</span> <span class=kn>import</span> <span class=n>Decimal</span>
 <span class=kn>import</span> <span class=nn>os</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
@@ -394,7 +394,7 @@
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>):</span>
     <span class=n>os</span><span class=o>.</span><span class=n>mkdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, save, load from decimal import Decimal import os import numpy as np import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b62380d1"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>define program with one atom, with constant detuning but variable Rabi frequency, where an initial pi/2 pulse is applied, followed by some time gap and a -pi/2 pulse. Note that the plateau time was chosen such that the area under the curve is pi/2 given given the constraint on how fast the Rabi frequency can change as well as the minimum allowed time step.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=feb5a466"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>plateau_time</span> <span class=o>=</span> <span class=p>(</span><span class=n>np</span><span class=o>.</span><span class=n>pi</span> <span class=o>/</span> <span class=mi>2</span> <span class=o>-</span> <span class=mf>0.625</span><span class=p>)</span> <span class=o>/</span> <span class=mf>12.5</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, save, load from decimal import Decimal import os import numpy as np import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=e3356d0a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>define program with one atom, with constant detuning but variable Rabi frequency, where an initial pi/2 pulse is applied, followed by some time gap and a -pi/2 pulse. Note that the plateau time was chosen such that the area under the curve is pi/2 given given the constraint on how fast the Rabi frequency can change as well as the minimum allowed time step.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b4371414"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>plateau_time</span> <span class=o>=</span> <span class=p>(</span><span class=n>np</span><span class=o>.</span><span class=n>pi</span> <span class=o>/</span> <span class=mi>2</span> <span class=o>-</span> <span class=mf>0.625</span><span class=p>)</span> <span class=o>/</span> <span class=mf>12.5</span>
 <span class=n>wf_durations</span> <span class=o>=</span> <span class=n>cast</span><span class=p>([</span><span class=mf>0.05</span><span class=p>,</span> <span class=n>plateau_time</span><span class=p>,</span> <span class=mf>0.05</span><span class=p>,</span> <span class=s2>"run_time"</span><span class=p>,</span> <span class=mf>0.05</span><span class=p>,</span> <span class=n>plateau_time</span><span class=p>,</span> <span class=mf>0.05</span><span class=p>])</span>
 <span class=n>rabi_wf_values</span> <span class=o>=</span> <span class=p>[</span><span class=mf>0.0</span><span class=p>,</span> <span class=mf>12.5</span><span class=p>,</span> <span class=mf>12.5</span><span class=p>,</span> <span class=mf>0.0</span><span class=p>]</span> <span class=o>*</span> <span class=mi>2</span>  <span class=c1># repeat values twice</span>
 
@@ -403,13 +403,13 @@
     <span class=o>.</span><span class=n>rydberg</span><span class=o>.</span><span class=n>rabi</span><span class=o>.</span><span class=n>amplitude</span><span class=o>.</span><span class=n>uniform</span><span class=o>.</span><span class=n>piecewise_linear</span><span class=p>(</span><span class=n>wf_durations</span><span class=p>,</span> <span class=n>rabi_wf_values</span><span class=p>)</span>
     <span class=o>.</span><span class=n>detuning</span><span class=o>.</span><span class=n>uniform</span><span class=o>.</span><span class=n>constant</span><span class=p>(</span><span class=mf>10.5</span><span class=p>,</span> <span class=nb>sum</span><span class=p>(</span><span class=n>wf_durations</span><span class=p>))</span>
 <span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2>plateau_time = (np.pi / 2 - 0.625) / 12.5 wf_durations = cast([0.05, plateau_time, 0.05, "run_time", 0.05, plateau_time, 0.05]) rabi_wf_values = [0.0, 12.5, 12.5, 0.0] * 2 # repeat values twice ramsey_program = ( start.add_position((0, 0)) .rydberg.rabi.amplitude.uniform.piecewise_linear(wf_durations, rabi_wf_values) .detuning.uniform.constant(10.5, sum(wf_durations)) )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a1d0e774"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Assign values to the variables in the program, allowing <code>run_time</code> (time gap between the two pi/2 pulses) to sweep across a range of values.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=97fef336"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>n_steps</span> <span class=o>=</span> <span class=mi>100</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2>plateau_time = (np.pi / 2 - 0.625) / 12.5 wf_durations = cast([0.05, plateau_time, 0.05, "run_time", 0.05, plateau_time, 0.05]) rabi_wf_values = [0.0, 12.5, 12.5, 0.0] * 2 # repeat values twice ramsey_program = ( start.add_position((0, 0)) .rydberg.rabi.amplitude.uniform.piecewise_linear(wf_durations, rabi_wf_values) .detuning.uniform.constant(10.5, sum(wf_durations)) )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6f684d8f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Assign values to the variables in the program, allowing <code>run_time</code> (time gap between the two pi/2 pulses) to sweep across a range of values.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=82f69c8b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>n_steps</span> <span class=o>=</span> <span class=mi>100</span>
 <span class=n>max_time</span> <span class=o>=</span> <span class=n>Decimal</span><span class=p>(</span><span class=s2>"3.0"</span><span class=p>)</span>
 <span class=n>dt</span> <span class=o>=</span> <span class=p>(</span><span class=n>max_time</span> <span class=o>-</span> <span class=n>Decimal</span><span class=p>(</span><span class=s2>"0.05"</span><span class=p>))</span> <span class=o>/</span> <span class=n>n_steps</span>
 <span class=n>run_times</span> <span class=o>=</span> <span class=p>[</span><span class=n>Decimal</span><span class=p>(</span><span class=s2>"0.05"</span><span class=p>)</span> <span class=o>+</span> <span class=n>dt</span> <span class=o>*</span> <span class=n>i</span> <span class=k>for</span> <span class=n>i</span> <span class=ow>in</span> <span class=nb>range</span><span class=p>(</span><span class=mi>101</span><span class=p>)]</span>
 
 <span class=n>ramsey_job</span> <span class=o>=</span> <span class=n>ramsey_program</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>run_time</span><span class=o>=</span><span class=n>run_times</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3>n_steps = 100 max_time = Decimal("3.0") dt = (max_time - Decimal("0.05")) / n_steps run_times = [Decimal("0.05") + dt * i for i in range(101)] ramsey_job = ramsey_program.batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4cce5849"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulation-and-hardware>Run Emulation and Hardware<a class=anchor-link href=#run-emulation-and-hardware>¶</a></h2><p>Like in the first tutorial, we will run the program on the emulator and hardware. Note that for the hardware we will use the <code>parallelize</code> method to run multiple copies of the program in parallel. For more information about this process, see the first tutorial.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=5e9dd6cc"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"ramsey-emulation.json"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3>n_steps = 100 max_time = Decimal("3.0") dt = (max_time - Decimal("0.05")) / n_steps run_times = [Decimal("0.05") + dt * i for i in range(101)] ramsey_job = ramsey_program.batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=04362298"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulation-and-hardware>Run Emulation and Hardware<a class=anchor-link href=#run-emulation-and-hardware>¶</a></h2><p>Like in the first tutorial, we will run the program on the emulator and hardware. Note that for the hardware we will use the <code>parallelize</code> method to run multiple copies of the program in parallel. For more information about this process, see the first tutorial.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=e529e643"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"ramsey-emulation.json"</span><span class=p>)</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>):</span>
     <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>ramsey_job</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>local_emulator</span><span class=p>()</span><span class=o>.</span><span class=n>run</span><span class=p>(</span><span class=mi>10000</span><span class=p>)</span>
@@ -419,11 +419,11 @@
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>hardware_filename</span><span class=p>):</span>
     <span class=n>batch</span> <span class=o>=</span> <span class=n>ramsey_job</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>100</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>batch</span><span class=p>,</span> <span class=n>hardware_filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4>emu_filename = os.path.join(os.path.abspath(""), "data", "ramsey-emulation.json") if not os.path.isfile(emu_filename): emu_batch = ramsey_job.braket.local_emulator().run(10000) save(emu_batch, emu_filename) hardware_filename = os.path.join(os.path.abspath(""), "data", "ramsey-job.json") if not os.path.isfile(hardware_filename): batch = ramsey_job.parallelize(24).braket.aquila().run_async(shots=100) save(batch, hardware_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f4118519"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plot-the-results>Plot the results<a class=anchor-link href=#plot-the-results>¶</a></h2><p>Exactly like in the Rabi Oscillation example, we can now plot the results from the hardware and emulation together. Again we will use the <code>report</code> to calculate the mean Rydberg population for each run, and then plot the results.</p> <p>first we load the results from the emulation and hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3032c658"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4>emu_filename = os.path.join(os.path.abspath(""), "data", "ramsey-emulation.json") if not os.path.isfile(emu_filename): emu_batch = ramsey_job.braket.local_emulator().run(10000) save(emu_batch, emu_filename) hardware_filename = os.path.join(os.path.abspath(""), "data", "ramsey-job.json") if not os.path.isfile(hardware_filename): batch = ramsey_job.parallelize(24).braket.aquila().run_async(shots=100) save(batch, hardware_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f97fae1e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plot-the-results>Plot the results<a class=anchor-link href=#plot-the-results>¶</a></h2><p>Exactly like in the Rabi Oscillation example, we can now plot the results from the hardware and emulation together. Again we will use the <code>report</code> to calculate the mean Rydberg population for each run, and then plot the results.</p> <p>first we load the results from the emulation and hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=afee90fe"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>hardware_filename</span><span class=p>)</span>
 <span class=c1># hardware_batch.fetch()</span>
 <span class=c1># save(filename, hardware_batch)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5>emu_batch = load(emu_filename) hardware_batch = load(hardware_filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b5daacf9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Next we can calculate the Rydberg population for each run and plot the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=03b984b6"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5>emu_batch = load(emu_filename) hardware_batch = load(hardware_filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f2bf71ea"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Next we can calculate the Rydberg population for each run and plot the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=52cf6b9a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>emulator_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 
 <span class=n>times</span> <span class=o>=</span> <span class=n>emulator_report</span><span class=o>.</span><span class=n>list_param</span><span class=p>(</span><span class=s2>"run_time"</span><span class=p>)</span>
@@ -438,4 +438,4 @@
 <span class=n>plt</span><span class=o>.</span><span class=n>ylabel</span><span class=p>(</span><span class=s2>"Rydberg population"</span><span class=p>)</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>legend</span><span class=p>()</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6> hardware_report = hardware_batch.report() emulator_report = emu_batch.report() times = emulator_report.list_param("run_time") density = [1 - ele.mean() for ele in emulator_report.bitstrings()] plt.plot(times, density, color="#878787", marker=".", label="Emulator") times = hardware_report.list_param("run_time") density = [1 - ele.mean() for ele in hardware_report.bitstrings()] plt.plot(times, density, color="#6437FF", linewidth=4, label="QPU") plt.xlabel("Time ($\mu s$)") plt.ylabel("Rydberg population") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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> 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+</pre></div> <div class=clipboard-copy-txt id=cell-6> hardware_report = hardware_batch.report() emulator_report = emu_batch.report() times = emulator_report.list_param("run_time") density = [1 - ele.mean() for ele in emulator_report.bitstrings()] plt.plot(times, density, color="#878787", marker=".", label="Emulator") times = hardware_report.list_param("run_time") density = [1 - ele.mean() for ele in hardware_report.bitstrings()] plt.plot(times, density, color="#6437FF", linewidth=4, label="QPU") plt.xlabel("Time ($\mu s$)") plt.ylabel("Rydberg population") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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"> 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69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> </a> <a href=https://pypi.org/project/bloqade/ target=_blank rel=noopener title=pypi.org class=md-social__link> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 448 512"><!-- Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path 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diff --git a/0.15.7/examples/example-2-multi-qubit-blockaded/index.html b/0.15.7/examples/example-2-multi-qubit-blockaded/index.html
index 9f26575..274daee 100644
--- a/0.15.7/examples/example-2-multi-qubit-blockaded/index.html
+++ b/0.15.7/examples/example-2-multi-qubit-blockaded/index.html
@@ -386,7 +386,7 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0dbe9d7e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-2-multi-qubit-blockaded.py> Download Script </a> <a class=md-button download href=../../assets/data/multi-qubit-blockaded-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=213b8471"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=multi-qubit-blockaded-rabi-oscillations>Multi-qubit Blockaded Rabi Oscillations<a class=anchor-link href=#multi-qubit-blockaded-rabi-oscillations>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this tutorial we will show you how to compose geometries with pulse sequences to perform multi-qubit blockaded Rabi oscillations. The Physics here is described in detail in the <a href=https://arxiv.org/abs/2306.11727>whitepaper</a>. But in short, we can use the Rydberg blockade to change the effective Rabi frequency of the entire system by adding more atoms to the cluster.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=2c667ad0"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b56fab7d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-2-multi-qubit-blockaded.py> Download Script </a> <a class=md-button download href=../../assets/data/multi-qubit-blockaded-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=188aab43"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=multi-qubit-blockaded-rabi-oscillations>Multi-qubit Blockaded Rabi Oscillations<a class=anchor-link href=#multi-qubit-blockaded-rabi-oscillations>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this tutorial we will show you how to compose geometries with pulse sequences to perform multi-qubit blockaded Rabi oscillations. The Physics here is described in detail in the <a href=https://arxiv.org/abs/2306.11727>whitepaper</a>. But in short, we can use the Rydberg blockade to change the effective Rabi frequency of the entire system by adding more atoms to the cluster.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8317aaa9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>from</span> <span class=nn>bloqade.atom_arrangement</span> <span class=kn>import</span> <span class=n>Chain</span><span class=p>,</span> <span class=n>Square</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
 <span class=kn>import</span> <span class=nn>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span>
@@ -395,7 +395,7 @@
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>):</span>
     <span class=n>os</span><span class=o>.</span><span class=n>mkdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, save, load from bloqade.atom_arrangement import Chain, Square import numpy as np import matplotlib.pyplot as plt import os if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=e6029c11"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-geometry>Defining the Geometry<a class=anchor-link href=#defining-the-geometry>¶</a></h2><p>We will start by defining the geometry of the atoms. The idea here is to cluster the atoms so that they are all blockaded from each other. Using a combination of the <code>Chain</code> and <code>Square</code> classes, as a base, one can add additional atoms to the geometry using the <code>add_position</code> method. This method takes a list of tuples, or a single tuple, of the form <code>(x,y)</code> where <code>x</code> and <code>y</code> are the coordinates of the atom in units of the lattice constant.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9385afd3"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>distance</span> <span class=o>=</span> <span class=mf>4.0</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, save, load from bloqade.atom_arrangement import Chain, Square import numpy as np import matplotlib.pyplot as plt import os if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=89f24d6d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-geometry>Defining the Geometry<a class=anchor-link href=#defining-the-geometry>¶</a></h2><p>We will start by defining the geometry of the atoms. The idea here is to cluster the atoms so that they are all blockaded from each other. Using a combination of the <code>Chain</code> and <code>Square</code> classes, as a base, one can add additional atoms to the geometry using the <code>add_position</code> method. This method takes a list of tuples, or a single tuple, of the form <code>(x,y)</code> where <code>x</code> and <code>y</code> are the coordinates of the atom in units of the lattice constant.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8b6257f6"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>distance</span> <span class=o>=</span> <span class=mf>4.0</span>
 <span class=n>inv_sqrt_2_rounded</span> <span class=o>=</span> <span class=mf>2.6</span>
 
 <span class=n>geometries</span> <span class=o>=</span> <span class=p>{</span>
@@ -417,17 +417,17 @@
         <span class=p>]</span>
     <span class=p>),</span>
 <span class=p>}</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2> distance = 4.0 inv_sqrt_2_rounded = 2.6 geometries = { 1: Chain(1), 2: Chain(2, lattice_spacing=distance), 3: start.add_position( [(-inv_sqrt_2_rounded, 0.0), (inv_sqrt_2_rounded, 0.0), (0, distance)] ), 4: Square(2, lattice_spacing=distance), 7: start.add_position( [ (0, 0), (distance, 0), (-0.5 * distance, distance), (0.5 * distance, distance), (1.5 * distance, distance), (0, 2 * distance), (distance, 2 * distance), ] ), }</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0aac650b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-pulse-sequence>Defining the Pulse Sequence<a class=anchor-link href=#defining-the-pulse-sequence>¶</a></h2><p>Next, we will define the pulse sequence. We start from the <code>start</code> object, which is an empty list of atom locations. In this case, we do not need atoms to build the pulse sequence, but to extract the sequence, we need to call the <code>parse_sequence</code> method. This creates a <code>Sequence</code> object that we can apply to multiple geometries.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8fb90c4e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>sequence</span> <span class=o>=</span> <span class=n>start</span><span class=o>.</span><span class=n>rydberg</span><span class=o>.</span><span class=n>rabi</span><span class=o>.</span><span class=n>amplitude</span><span class=o>.</span><span class=n>uniform</span><span class=o>.</span><span class=n>piecewise_linear</span><span class=p>(</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2> distance = 4.0 inv_sqrt_2_rounded = 2.6 geometries = { 1: Chain(1), 2: Chain(2, lattice_spacing=distance), 3: start.add_position( [(-inv_sqrt_2_rounded, 0.0), (inv_sqrt_2_rounded, 0.0), (0, distance)] ), 4: Square(2, lattice_spacing=distance), 7: start.add_position( [ (0, 0), (distance, 0), (-0.5 * distance, distance), (0.5 * distance, distance), (1.5 * distance, distance), (0, 2 * distance), (distance, 2 * distance), ] ), }</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f6999584"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-pulse-sequence>Defining the Pulse Sequence<a class=anchor-link href=#defining-the-pulse-sequence>¶</a></h2><p>Next, we will define the pulse sequence. We start from the <code>start</code> object, which is an empty list of atom locations. In this case, we do not need atoms to build the pulse sequence, but to extract the sequence, we need to call the <code>parse_sequence</code> method. This creates a <code>Sequence</code> object that we can apply to multiple geometries.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ef532368"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>sequence</span> <span class=o>=</span> <span class=n>start</span><span class=o>.</span><span class=n>rydberg</span><span class=o>.</span><span class=n>rabi</span><span class=o>.</span><span class=n>amplitude</span><span class=o>.</span><span class=n>uniform</span><span class=o>.</span><span class=n>piecewise_linear</span><span class=p>(</span>
     <span class=n>durations</span><span class=o>=</span><span class=p>[</span><span class=s2>"ramp_time"</span><span class=p>,</span> <span class=s2>"run_time"</span><span class=p>,</span> <span class=s2>"ramp_time"</span><span class=p>],</span>
     <span class=n>values</span><span class=o>=</span><span class=p>[</span><span class=mf>0.0</span><span class=p>,</span> <span class=s2>"rabi_drive"</span><span class=p>,</span> <span class=s2>"rabi_drive"</span><span class=p>,</span> <span class=mf>0.0</span><span class=p>],</span>
 <span class=p>)</span><span class=o>.</span><span class=n>parse_sequence</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3>sequence = start.rydberg.rabi.amplitude.uniform.piecewise_linear( durations=["ramp_time", "run_time", "ramp_time"], values=[0.0, "rabi_drive", "rabi_drive", 0.0], ).parse_sequence()</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=484eb567"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-program>Defining the Program<a class=anchor-link href=#defining-the-program>¶</a></h2><p>Now, all that is left to do is to compose the geometry and the Pulse sequence into a fully defined program. We can do this by calling the <code>apply</code> method on the geometry and passing in the sequence. This method will return an object that can then be assigned parameters.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=48ad4f93"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>batch</span> <span class=o>=</span> <span class=p>(</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3>sequence = start.rydberg.rabi.amplitude.uniform.piecewise_linear( durations=["ramp_time", "run_time", "ramp_time"], values=[0.0, "rabi_drive", "rabi_drive", 0.0], ).parse_sequence()</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=72cb35ac"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-program>Defining the Program<a class=anchor-link href=#defining-the-program>¶</a></h2><p>Now, all that is left to do is to compose the geometry and the Pulse sequence into a fully defined program. We can do this by calling the <code>apply</code> method on the geometry and passing in the sequence. This method will return an object that can then be assigned parameters.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=fd067048"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>batch</span> <span class=o>=</span> <span class=p>(</span>
     <span class=n>geometries</span><span class=p>[</span><span class=mi>7</span><span class=p>]</span>
     <span class=o>.</span><span class=n>apply</span><span class=p>(</span><span class=n>sequence</span><span class=p>)</span>
     <span class=o>.</span><span class=n>assign</span><span class=p>(</span><span class=n>ramp_time</span><span class=o>=</span><span class=mf>0.06</span><span class=p>,</span> <span class=n>rabi_drive</span><span class=o>=</span><span class=mi>5</span><span class=p>)</span>
     <span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>run_time</span><span class=o>=</span><span class=mf>0.05</span> <span class=o>*</span> <span class=n>np</span><span class=o>.</span><span class=n>arange</span><span class=p>(</span><span class=mi>21</span><span class=p>))</span>
 <span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4>batch = ( geometries[7] .apply(sequence) .assign(ramp_time=0.06, rabi_drive=5) .batch_assign(run_time=0.05 * np.arange(21)) )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=216278b2"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>Again, we run the program on the emulator and save the results to a file. for the emyulator and Aquila. Save the results to a file so that we can use them later.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=76a7a4e5"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4>batch = ( geometries[7] .apply(sequence) .assign(ramp_time=0.06, rabi_drive=5) .batch_assign(run_time=0.05 * np.arange(21)) )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=173ca708"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>Again, we run the program on the emulator and save the results to a file. for the emyulator and Aquila. Save the results to a file so that we can use them later.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a81aac31"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
     <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"multi-qubit-blockaded-emulation.json"</span>
 <span class=p>)</span>
 
@@ -440,11 +440,11 @@
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>filename</span><span class=p>):</span>
     <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>100</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>hardware_batch</span><span class=p>,</span> <span class=n>filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5> emu_filename = os.path.join( os.path.abspath(""), "data", "multi-qubit-blockaded-emulation.json" ) if not os.path.isfile(emu_filename): emu_batch = batch.braket.local_emulator().run(10000) save(emu_batch, emu_filename) filename = os.path.join(os.path.abspath(""), "data", "multi-qubit-blockaded-job.json") if not os.path.isfile(filename): hardware_batch = batch.parallelize(24).braket.aquila().run_async(shots=100) save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3d69265c"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>First, we load the results from the file.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=93c71882"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5> emu_filename = os.path.join( os.path.abspath(""), "data", "multi-qubit-blockaded-emulation.json" ) if not os.path.isfile(emu_filename): emu_batch = batch.braket.local_emulator().run(10000) save(emu_batch, emu_filename) filename = os.path.join(os.path.abspath(""), "data", "multi-qubit-blockaded-job.json") if not os.path.isfile(filename): hardware_batch = batch.parallelize(24).braket.aquila().run_async(shots=100) save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=58bedcb8"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>First, we load the results from the file.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=6044946d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>filename</span><span class=p>)</span>
 <span class=c1># hardware_batch.fetch()</span>
 <span class=c1># save(filename, hardware_batch)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6>emu_batch = load(emu_filename) hardware_batch = load(filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=84b7b637"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>The quantity of interest here is the total Rydberg density of the cluster defined as the sum of the Rydberg densities of each atom. We can extract this from the results and plot it as a function of time. We will do this for both the emulator and the hardware. We can use the <code>rydberg_densities</code> function to extract the densities from the <code>Report</code> of the <code>batch</code> object.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=50a2344e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-6>emu_batch = load(emu_filename) hardware_batch = load(filename) # hardware_batch.fetch() # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b7afcfb4"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>The quantity of interest here is the total Rydberg density of the cluster defined as the sum of the Rydberg densities of each atom. We can extract this from the results and plot it as a function of time. We will do this for both the emulator and the hardware. We can use the <code>rydberg_densities</code> function to extract the densities from the <code>Report</code> of the <code>batch</code> object.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=1b67c157"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>emu_densities</span> <span class=o>=</span> <span class=n>emu_report</span><span class=o>.</span><span class=n>rydberg_densities</span><span class=p>()</span>
 <span class=n>emu_densities_summed</span> <span class=o>=</span> <span class=n>emu_densities</span><span class=o>.</span><span class=n>sum</span><span class=p>(</span><span class=n>axis</span><span class=o>=</span><span class=mi>1</span><span class=p>)</span>
 
@@ -467,4 +467,4 @@
 <span class=n>ax</span><span class=o>.</span><span class=n>set_xlabel</span><span class=p>(</span><span class=s2>"Time ($\mu s$)"</span><span class=p>)</span>
 <span class=n>ax</span><span class=o>.</span><span class=n>set_ylabel</span><span class=p>(</span><span class=s2>"Sum of Rydberg Densities"</span><span class=p>)</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-7> emu_report = emu_batch.report() emu_densities = emu_report.rydberg_densities() emu_densities_summed = emu_densities.sum(axis=1) hardware_report = hardware_batch.report() hardware_densities = hardware_report.rydberg_densities() hardware_densities_summed = hardware_densities.sum(axis=1) emu_run_times = emu_report.list_param("run_time") hw_run_times = hardware_report.list_param("run_time") fig, ax = plt.subplots() ax.set_xlabel("Time") ax.set_ylabel("Sum of Rydberg Densities") # emulation ax.plot(emu_run_times, emu_densities_summed, label="Emulator", color="#878787") # hardware ax.plot(hw_run_times, hardware_densities_summed, label="QPU", color="#6437FF") ax.legend() ax.set_xlabel("Time ($\mu s$)") ax.set_ylabel("Sum of Rydberg Densities") plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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"> 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+</pre></div> <div class=clipboard-copy-txt id=cell-7> emu_report = emu_batch.report() emu_densities = emu_report.rydberg_densities() emu_densities_summed = emu_densities.sum(axis=1) hardware_report = hardware_batch.report() hardware_densities = hardware_report.rydberg_densities() hardware_densities_summed = hardware_densities.sum(axis=1) emu_run_times = emu_report.list_param("run_time") hw_run_times = hardware_report.list_param("run_time") fig, ax = plt.subplots() ax.set_xlabel("Time") ax.set_ylabel("Sum of Rydberg Densities") # emulation ax.plot(emu_run_times, emu_densities_summed, label="Emulator", color="#878787") # hardware ax.plot(hw_run_times, hardware_densities_summed, label="QPU", color="#6437FF") ax.legend() ax.set_xlabel("Time ($\mu s$)") ax.set_ylabel("Sum of Rydberg Densities") plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea 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"> 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diff --git a/0.15.7/examples/example-2-nonequilibrium-dynamics-blockade-radius/index.html b/0.15.7/examples/example-2-nonequilibrium-dynamics-blockade-radius/index.html
index 74808e2..c8b7a8f 100644
--- a/0.15.7/examples/example-2-nonequilibrium-dynamics-blockade-radius/index.html
+++ b/0.15.7/examples/example-2-nonequilibrium-dynamics-blockade-radius/index.html
@@ -386,7 +386,7 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=08a2e1bb"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-2-nonequilibrium-dynamics-blockade-radius.py> Download Script </a> <a class=md-button download href=../../assets/data/nonequilibrium-dynamics-blockade-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=42f59510"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=nonequilibrium-dynamics-of-nearly-blockaded-rydberg-atoms>Nonequilibrium Dynamics of nearly Blockaded Rydberg Atoms<a class=anchor-link href=#nonequilibrium-dynamics-of-nearly-blockaded-rydberg-atoms>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we will show how to generate multi-atom programs looking specifically at the dynamics of two atoms that are right on the blockade radius. First lsets start with the imports.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a2d48808"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=46a741a9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-2-nonequilibrium-dynamics-blockade-radius.py> Download Script </a> <a class=md-button download href=../../assets/data/nonequilibrium-dynamics-blockade-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9e75fc54"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=nonequilibrium-dynamics-of-nearly-blockaded-rydberg-atoms>Nonequilibrium Dynamics of nearly Blockaded Rydberg Atoms<a class=anchor-link href=#nonequilibrium-dynamics-of-nearly-blockaded-rydberg-atoms>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we will show how to generate multi-atom programs looking specifically at the dynamics of two atoms that are right on the blockade radius. First lsets start with the imports.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=4011e000"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>from</span> <span class=nn>bloqade.atom_arrangement</span> <span class=kn>import</span> <span class=n>Chain</span>
 
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
@@ -396,7 +396,7 @@
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>):</span>
     <span class=n>os</span><span class=o>.</span><span class=n>mkdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1> from bloqade import save, load from bloqade.atom_arrangement import Chain import numpy as np import matplotlib.pyplot as plt import os if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a14bd287"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=program-definition>Program Definition<a class=anchor-link href=#program-definition>¶</a></h2><p>We will start by defining a program. We set up a chain of two atoms with a parmaeterized distance between them. We then define a Rabi like in the original Rabi oscillation example. Given a <code>rabi_ampl</code> of 15 rad/µs the blockaded radius s 8.44 µm. We will look at the dynamics of the system for a distance of 8.5 µm to be every so slightly outside of the blockade radius. We then define a <code>batch</code> of programs for different <code>run_time</code> values.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b4cf2353"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>initial_geometry</span> <span class=o>=</span> <span class=n>Chain</span><span class=p>(</span><span class=mi>2</span><span class=p>,</span> <span class=n>lattice_spacing</span><span class=o>=</span><span class=s2>"distance"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1> from bloqade import save, load from bloqade.atom_arrangement import Chain import numpy as np import matplotlib.pyplot as plt import os if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=631cea7a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=program-definition>Program Definition<a class=anchor-link href=#program-definition>¶</a></h2><p>We will start by defining a program. We set up a chain of two atoms with a parmaeterized distance between them. We then define a Rabi like in the original Rabi oscillation example. Given a <code>rabi_ampl</code> of 15 rad/µs the blockaded radius s 8.44 µm. We will look at the dynamics of the system for a distance of 8.5 µm to be every so slightly outside of the blockade radius. We then define a <code>batch</code> of programs for different <code>run_time</code> values.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a7f35a0b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>initial_geometry</span> <span class=o>=</span> <span class=n>Chain</span><span class=p>(</span><span class=mi>2</span><span class=p>,</span> <span class=n>lattice_spacing</span><span class=o>=</span><span class=s2>"distance"</span><span class=p>)</span>
 <span class=n>program_waveforms</span> <span class=o>=</span> <span class=n>initial_geometry</span><span class=o>.</span><span class=n>rydberg</span><span class=o>.</span><span class=n>rabi</span><span class=o>.</span><span class=n>amplitude</span><span class=o>.</span><span class=n>uniform</span><span class=o>.</span><span class=n>piecewise_linear</span><span class=p>(</span>
     <span class=n>durations</span><span class=o>=</span><span class=p>[</span><span class=s2>"ramp_time"</span><span class=p>,</span> <span class=s2>"run_time"</span><span class=p>,</span> <span class=s2>"ramp_time"</span><span class=p>],</span>
     <span class=n>values</span><span class=o>=</span><span class=p>[</span><span class=mf>0.0</span><span class=p>,</span> <span class=s2>"rabi_ampl"</span><span class=p>,</span> <span class=s2>"rabi_ampl"</span><span class=p>,</span> <span class=mf>0.0</span><span class=p>],</span>
@@ -405,20 +405,20 @@
     <span class=n>ramp_time</span><span class=o>=</span><span class=mf>0.06</span><span class=p>,</span> <span class=n>rabi_ampl</span><span class=o>=</span><span class=mi>15</span><span class=p>,</span> <span class=n>distance</span><span class=o>=</span><span class=mf>8.5</span>
 <span class=p>)</span>
 <span class=n>batch</span> <span class=o>=</span> <span class=n>program_assigned_vars</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>run_time</span><span class=o>=</span><span class=mf>0.05</span> <span class=o>*</span> <span class=n>np</span><span class=o>.</span><span class=n>arange</span><span class=p>(</span><span class=mi>31</span><span class=p>))</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2> initial_geometry = Chain(2, lattice_spacing="distance") program_waveforms = initial_geometry.rydberg.rabi.amplitude.uniform.piecewise_linear( durations=["ramp_time", "run_time", "ramp_time"], values=[0.0, "rabi_ampl", "rabi_ampl", 0.0], ) program_assigned_vars = program_waveforms.assign( ramp_time=0.06, rabi_ampl=15, distance=8.5 ) batch = program_assigned_vars.batch_assign(run_time=0.05 * np.arange(31))</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=7c5abce4"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>Once again we will run the emulator and hardware. We will use the <code>local_emulator</code> method to run the emulator locally. We will then save the results to a file so that we can use them later.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=101becc2"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2> initial_geometry = Chain(2, lattice_spacing="distance") program_waveforms = initial_geometry.rydberg.rabi.amplitude.uniform.piecewise_linear( durations=["ramp_time", "run_time", "ramp_time"], values=[0.0, "rabi_ampl", "rabi_ampl", 0.0], ) program_assigned_vars = program_waveforms.assign( ramp_time=0.06, rabi_ampl=15, distance=8.5 ) batch = program_assigned_vars.batch_assign(run_time=0.05 * np.arange(31))</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1712a6f8"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-emulator-and-hardware>Run Emulator and Hardware<a class=anchor-link href=#run-emulator-and-hardware>¶</a></h2><p>Once again we will run the emulator and hardware. We will use the <code>local_emulator</code> method to run the emulator locally. We will then save the results to a file so that we can use them later.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8f702fe4"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
     <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"nonequilibrium-dynamics-blockade-emulation.json"</span>
 <span class=p>)</span>
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>):</span>
     <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>local_emulator</span><span class=p>()</span><span class=o>.</span><span class=n>run</span><span class=p>(</span><span class=mi>10000</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>emu_batch</span><span class=p>,</span> <span class=n>emu_filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3>emu_filename = os.path.join( os.path.abspath(""), "data", "nonequilibrium-dynamics-blockade-emulation.json" ) if not os.path.isfile(emu_filename): emu_batch = batch.braket.local_emulator().run(10000) save(emu_batch, emu_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6623e595"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>When running on the hardware we will also parallelize the batch and submit.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9fc39824"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3>emu_filename = os.path.join( os.path.abspath(""), "data", "nonequilibrium-dynamics-blockade-emulation.json" ) if not os.path.isfile(emu_filename): emu_batch = batch.braket.local_emulator().run(10000) save(emu_batch, emu_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3d1ba50b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>When running on the hardware we will also parallelize the batch and submit.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=436b9d03"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
     <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"nonequilibrium-dynamics-blockade-job.json"</span>
 <span class=p>)</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>filename</span><span class=p>):</span>
     <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>100</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>hardware_batch</span><span class=p>,</span> <span class=n>filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4>filename = os.path.join( os.path.abspath(""), "data", "nonequilibrium-dynamics-blockade-job.json" ) if not os.path.isfile(filename): hardware_batch = batch.parallelize(24).braket.aquila().run_async(shots=100) save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0577d33d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>In order to show the complex dynamics we will plot the probability of having <code>0</code>, <code>1</code> , or <code>2</code> Rydberg atoms as a function of time. We will do this for both the emulator and the hardware. We can use the <code>rydberg_state_probabilities</code> function to extract the probabilities from the counts. This function takes a list of counts and returns a dictionary of probabilities for each state. The counts are obtained from the <code>report</code> of the <code>batch</code> object.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=cbd96240"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>rydberg_state_probabilities</span><span class=p>(</span><span class=n>shot_counts</span><span class=p>):</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4>filename = os.path.join( os.path.abspath(""), "data", "nonequilibrium-dynamics-blockade-job.json" ) if not os.path.isfile(filename): hardware_batch = batch.parallelize(24).braket.aquila().run_async(shots=100) save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4c006836"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>In order to show the complex dynamics we will plot the probability of having <code>0</code>, <code>1</code> , or <code>2</code> Rydberg atoms as a function of time. We will do this for both the emulator and the hardware. We can use the <code>rydberg_state_probabilities</code> function to extract the probabilities from the counts. This function takes a list of counts and returns a dictionary of probabilities for each state. The counts are obtained from the <code>report</code> of the <code>batch</code> object.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=73dd4118"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>rydberg_state_probabilities</span><span class=p>(</span><span class=n>shot_counts</span><span class=p>):</span>
     <span class=n>probabilities_dict</span> <span class=o>=</span> <span class=p>{</span><span class=s2>"0"</span><span class=p>:</span> <span class=p>[],</span> <span class=s2>"1"</span><span class=p>:</span> <span class=p>[],</span> <span class=s2>"2"</span><span class=p>:</span> <span class=p>[]}</span>
 
     <span class=c1># iterate over each of the task results</span>
@@ -433,20 +433,20 @@
         <span class=n>probabilities_dict</span><span class=p>[</span><span class=s2>"2"</span><span class=p>]</span><span class=o>.</span><span class=n>append</span><span class=p>(</span><span class=n>task_result</span><span class=o>.</span><span class=n>get</span><span class=p>(</span><span class=s2>"00"</span><span class=p>,</span> <span class=mi>0</span><span class=p>)</span> <span class=o>/</span> <span class=n>total_shots</span><span class=p>)</span>
 
     <span class=k>return</span> <span class=n>probabilities_dict</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5>def rydberg_state_probabilities(shot_counts): probabilities_dict = {"0": [], "1": [], "2": []} # iterate over each of the task results for task_result in shot_counts: # get total number of shots total_shots = sum(task_result.values()) # get probability of each state probabilities_dict["0"].append(task_result.get("11", 0) / total_shots) probabilities_dict["1"].append( (task_result.get("10", 0) + task_result.get("01", 0)) / total_shots ) probabilities_dict["2"].append(task_result.get("00", 0) / total_shots) return probabilities_dict</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=bae6a85e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=extracting-the-counts-and-probabilities>Extracting the counts and probabilities<a class=anchor-link href=#extracting-the-counts-and-probabilities>¶</a></h2><p>We will now extract the counts and probabilities from the emulator and hardware runs. We will then plot the results. First we load the data from the files.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=f58cd2be"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5>def rydberg_state_probabilities(shot_counts): probabilities_dict = {"0": [], "1": [], "2": []} # iterate over each of the task results for task_result in shot_counts: # get total number of shots total_shots = sum(task_result.values()) # get probability of each state probabilities_dict["0"].append(task_result.get("11", 0) / total_shots) probabilities_dict["1"].append( (task_result.get("10", 0) + task_result.get("01", 0)) / total_shots ) probabilities_dict["2"].append(task_result.get("00", 0) / total_shots) return probabilities_dict</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1b86850b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=extracting-the-counts-and-probabilities>Extracting the counts and probabilities<a class=anchor-link href=#extracting-the-counts-and-probabilities>¶</a></h2><p>We will now extract the counts and probabilities from the emulator and hardware runs. We will then plot the results. First we load the data from the files.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=e12e5bf9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>emu_counts</span> <span class=o>=</span> <span class=n>emu_report</span><span class=o>.</span><span class=n>counts</span><span class=p>()</span>
 
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>filename</span><span class=p>)</span>
 <span class=c1># hardware_batch.fetch() # uncomment to fetch results from Braket</span>
 <span class=c1># save(filename, hardware_batch)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6> emu_batch = load(emu_filename) emu_report = emu_batch.report() emu_counts = emu_report.counts() hardware_batch = load(filename) # hardware_batch.fetch() # uncomment to fetch results from Braket # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2f02e484"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>To get the <code>counts</code> we need to get a <code>report</code> from the <code>batch</code> objects. Then with the report we can get the counts. The counts are a dictionary that maps the bitstring to the number of times that bitstring was measured.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a8371afe"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-6> emu_batch = load(emu_filename) emu_report = emu_batch.report() emu_counts = emu_report.counts() hardware_batch = load(filename) # hardware_batch.fetch() # uncomment to fetch results from Braket # save(filename, hardware_batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=7116c31e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>To get the <code>counts</code> we need to get a <code>report</code> from the <code>batch</code> objects. Then with the report we can get the counts. The counts are a dictionary that maps the bitstring to the number of times that bitstring was measured.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=1c380de9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 
 
 <span class=n>emu_rydberg_state_probabilities</span> <span class=o>=</span> <span class=n>rydberg_state_probabilities</span><span class=p>(</span><span class=n>emu_report</span><span class=o>.</span><span class=n>counts</span><span class=p>())</span>
 <span class=n>hw_rydberg_state_probabilities</span> <span class=o>=</span> <span class=n>rydberg_state_probabilities</span><span class=p>(</span><span class=n>hardware_report</span><span class=o>.</span><span class=n>counts</span><span class=p>())</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-7> emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_rydberg_state_probabilities = rydberg_state_probabilities(emu_report.counts()) hw_rydberg_state_probabilities = rydberg_state_probabilities(hardware_report.counts())</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=17b885cf"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>plot 0, 1, and 2 Rydberg state probabilities but in separate plots</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=0e869257"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-8> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>figure</span><span class=p>,</span> <span class=n>axs</span> <span class=o>=</span> <span class=n>plt</span><span class=o>.</span><span class=n>subplots</span><span class=p>(</span><span class=mi>1</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=n>figsize</span><span class=o>=</span><span class=p>(</span><span class=mi>12</span><span class=p>,</span> <span class=mi>6</span><span class=p>),</span> <span class=n>sharey</span><span class=o>=</span><span class=kc>True</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-7> emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_rydberg_state_probabilities = rydberg_state_probabilities(emu_report.counts()) hw_rydberg_state_probabilities = rydberg_state_probabilities(hardware_report.counts())</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=fdd9ad75"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>plot 0, 1, and 2 Rydberg state probabilities but in separate plots</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=78ad6366"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-8> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>figure</span><span class=p>,</span> <span class=n>axs</span> <span class=o>=</span> <span class=n>plt</span><span class=o>.</span><span class=n>subplots</span><span class=p>(</span><span class=mi>1</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=n>figsize</span><span class=o>=</span><span class=p>(</span><span class=mi>12</span><span class=p>,</span> <span class=mi>6</span><span class=p>),</span> <span class=n>sharey</span><span class=o>=</span><span class=kc>True</span><span class=p>)</span>
 
 <span class=n>emu_run_times</span> <span class=o>=</span> <span class=n>emu_report</span><span class=o>.</span><span class=n>list_param</span><span class=p>(</span><span class=s2>"run_time"</span><span class=p>)</span>
 <span class=n>hardware_run_times</span> <span class=o>=</span> <span class=n>hardware_report</span><span class=o>.</span><span class=n>list_param</span><span class=p>(</span><span class=s2>"run_time"</span><span class=p>)</span>
@@ -478,4 +478,4 @@
 <span class=n>ax</span><span class=o>.</span><span class=n>legend</span><span class=p>(</span><span class=n>handles</span><span class=o>=</span><span class=p>[</span><span class=o>*</span><span class=n>hw_lines</span><span class=p>,</span> <span class=n>emu_lines</span><span class=p>[</span><span class=o>-</span><span class=mi>1</span><span class=p>]])</span>
 
 <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-8>figure, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True) emu_run_times = emu_report.list_param("run_time") hardware_run_times = hardware_report.list_param("run_time") emu_colors = ["#55DE79", "#EDFF1A", "#C2477F"] # Green, Yellow, Red emu_lines = [] hw_lines = [] for ax, rydberg_state, color in zip(axs, ["0", "1", "2"], emu_colors): (hw_line,) = ax.plot( emu_run_times, hw_rydberg_state_probabilities[rydberg_state], label=rydberg_state + "-Rydberg QPU", color=color, ) (emu_line,) = ax.plot( hardware_run_times, emu_rydberg_state_probabilities[rydberg_state], color="#878787", ) emu_line.set_label("Emulator") emu_lines.append(emu_line) hw_lines.append(hw_line) ax.set_xlabel("time ($\mu s$)") ax.set_ylabel("Probability") ax.legend(handles=[*hw_lines, emu_lines[-1]]) plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div 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"> 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+</pre></div> <div class=clipboard-copy-txt id=cell-8>figure, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True) emu_run_times = emu_report.list_param("run_time") hardware_run_times = hardware_report.list_param("run_time") emu_colors = ["#55DE79", "#EDFF1A", "#C2477F"] # Green, Yellow, Red emu_lines = [] hw_lines = [] for ax, rydberg_state, color in zip(axs, ["0", "1", "2"], emu_colors): (hw_line,) = ax.plot( emu_run_times, hw_rydberg_state_probabilities[rydberg_state], label=rydberg_state + "-Rydberg QPU", color=color, ) (emu_line,) = ax.plot( hardware_run_times, emu_rydberg_state_probabilities[rydberg_state], color="#878787", ) emu_line.set_label("Emulator") emu_lines.append(emu_line) hw_lines.append(hw_line) ax.set_xlabel("time ($\mu s$)") ax.set_ylabel("Probability") ax.legend(handles=[*hw_lines, emu_lines[-1]]) plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div 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"> 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diff --git a/0.15.7/examples/example-2-two-qubit-adiabatic/index.html b/0.15.7/examples/example-2-two-qubit-adiabatic/index.html
index 1bf5d15..2a5ee23 100644
--- a/0.15.7/examples/example-2-two-qubit-adiabatic/index.html
+++ b/0.15.7/examples/example-2-two-qubit-adiabatic/index.html
@@ -386,7 +386,7 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=997793af"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-2-two-qubit-adiabatic.py> Download Script </a> <a class=md-button download href=../../assets/data/two-qubit-adiabatic-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=634204ae"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=two-qubit-adiabatic-sweep>Two Qubit Adiabatic Sweep<a class=anchor-link href=#two-qubit-adiabatic-sweep>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example, we show how to use Bloqade to program an adiabatic sweep on a pair of atoms, with the distance between atoms gradually increasing per task. This will allow us to explore the effect of the Rydberg interaction. We will run the program on both the emulator and the hardware to compare the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=1e8294bb"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>var</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=252feea1"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-2-two-qubit-adiabatic.py> Download Script </a> <a class=md-button download href=../../assets/data/two-qubit-adiabatic-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6f07e2ae"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=two-qubit-adiabatic-sweep>Two Qubit Adiabatic Sweep<a class=anchor-link href=#two-qubit-adiabatic-sweep>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example, we show how to use Bloqade to program an adiabatic sweep on a pair of atoms, with the distance between atoms gradually increasing per task. This will allow us to explore the effect of the Rydberg interaction. We will run the program on both the emulator and the hardware to compare the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=0ce29eea"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>start</span><span class=p>,</span> <span class=n>cast</span><span class=p>,</span> <span class=n>var</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
 
 <span class=kn>import</span> <span class=nn>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span>
@@ -395,7 +395,7 @@
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>):</span>
     <span class=n>os</span><span class=o>.</span><span class=n>mkdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, var, save, load import numpy as np import matplotlib.pyplot as plt import os if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=98d05c19"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-program>Defining the Program<a class=anchor-link href=#defining-the-program>¶</a></h2><p>Now, we define our program of interest. For an adiabatic protocol, we keep that Rabi frequency at a considerable value while slowly ramping the detuning from a large negative to a positive value. The idea is that when the detuning is large and negative the atoms remain in the ground state. As the detuning is ramped to positive values, the atoms are able to be excited to the Rydberg state, however if the atoms are too close together, the Rydberg interactions effectively acts like a negative etuning to neighboring atoms, preventing them from being excited. This is the blockade effect. For atoms that are sufficiently far apart, the Rydberg interaction is negligible and the atoms can be excited to the Rydberg state. As the atoms get closer together, the Rydberg interaction becomes more significant the probability of exciting both atoms becomes smaller. The typical length scale for the cross over from the non-interacting to the blockade regime is the blockade radius.</p> <p>Note that you can perform arithmetic operations directly on variables in the program but this requires the variable to be explicitly declared by passing a string to the <code>var</code> function and THEN doing arithmetic on it.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=db51e4d2"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>detuning_value</span> <span class=o>=</span> <span class=n>var</span><span class=p>(</span><span class=s2>"detuning_value"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import start, cast, var, save, load import numpy as np import matplotlib.pyplot as plt import os if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=172d374a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=defining-the-program>Defining the Program<a class=anchor-link href=#defining-the-program>¶</a></h2><p>Now, we define our program of interest. For an adiabatic protocol, we keep that Rabi frequency at a considerable value while slowly ramping the detuning from a large negative to a positive value. The idea is that when the detuning is large and negative the atoms remain in the ground state. As the detuning is ramped to positive values, the atoms are able to be excited to the Rydberg state, however if the atoms are too close together, the Rydberg interactions effectively acts like a negative etuning to neighboring atoms, preventing them from being excited. This is the blockade effect. For atoms that are sufficiently far apart, the Rydberg interaction is negligible and the atoms can be excited to the Rydberg state. As the atoms get closer together, the Rydberg interaction becomes more significant the probability of exciting both atoms becomes smaller. The typical length scale for the cross over from the non-interacting to the blockade regime is the blockade radius.</p> <p>Note that you can perform arithmetic operations directly on variables in the program but this requires the variable to be explicitly declared by passing a string to the <code>var</code> function and THEN doing arithmetic on it.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=758a877f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>detuning_value</span> <span class=o>=</span> <span class=n>var</span><span class=p>(</span><span class=s2>"detuning_value"</span><span class=p>)</span>
 <span class=n>durations</span> <span class=o>=</span> <span class=n>cast</span><span class=p>([</span><span class=s2>"ramp_time"</span><span class=p>,</span> <span class=s2>"run_time"</span><span class=p>,</span> <span class=s2>"ramp_time"</span><span class=p>])</span>
 <span class=n>prog</span> <span class=o>=</span> <span class=p>(</span>
     <span class=n>start</span><span class=o>.</span><span class=n>add_position</span><span class=p>([(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>0</span><span class=p>),</span> <span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=s2>"atom_distance"</span><span class=p>)])</span>
@@ -417,7 +417,7 @@
 <span class=n>batch</span> <span class=o>=</span> <span class=n>prog</span><span class=o>.</span><span class=n>assign</span><span class=p>(</span>
     <span class=n>ramp_time</span><span class=o>=</span><span class=mf>1.0</span><span class=p>,</span> <span class=n>run_time</span><span class=o>=</span><span class=mf>2.0</span><span class=p>,</span> <span class=n>rabi_value</span><span class=o>=</span><span class=mf>15.0</span><span class=p>,</span> <span class=n>detuning_value</span><span class=o>=</span><span class=mf>15.0</span>
 <span class=p>)</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>atom_distance</span><span class=o>=</span><span class=n>distances</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2> detuning_value = var("detuning_value") durations = cast(["ramp_time", "run_time", "ramp_time"]) prog = ( start.add_position([(0, 0), (0, "atom_distance")]) .rydberg.rabi.amplitude.uniform.piecewise_linear( durations=durations, values=[0, "rabi_value", "rabi_value", 0] ) .detuning.uniform.piecewise_linear( durations=durations, values=[ -detuning_value, -detuning_value, detuning_value, detuning_value, ], ) ) distances = np.arange(4, 11, 1) batch = prog.assign( ramp_time=1.0, run_time=2.0, rabi_value=15.0, detuning_value=15.0 ).batch_assign(atom_distance=distances)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=fccb1e1e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-on-emulator-and-hardware>Run on Emulator and Hardware<a class=anchor-link href=#run-on-emulator-and-hardware>¶</a></h2><p>In previous examples, we have shown how to run a program on the emulator and hardware. First, we will run the program on the emulator and save the results to a file.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=2997f609"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># get emulation batch, running 1000 shots per task</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2> detuning_value = var("detuning_value") durations = cast(["ramp_time", "run_time", "ramp_time"]) prog = ( start.add_position([(0, 0), (0, "atom_distance")]) .rydberg.rabi.amplitude.uniform.piecewise_linear( durations=durations, values=[0, "rabi_value", "rabi_value", 0] ) .detuning.uniform.piecewise_linear( durations=durations, values=[ -detuning_value, -detuning_value, detuning_value, detuning_value, ], ) ) distances = np.arange(4, 11, 1) batch = prog.assign( ramp_time=1.0, run_time=2.0, rabi_value=15.0, detuning_value=15.0 ).batch_assign(atom_distance=distances)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=02dff960"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-on-emulator-and-hardware>Run on Emulator and Hardware<a class=anchor-link href=#run-on-emulator-and-hardware>¶</a></h2><p>In previous examples, we have shown how to run a program on the emulator and hardware. First, we will run the program on the emulator and save the results to a file.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=61e44349"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># get emulation batch, running 1000 shots per task</span>
 <span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
     <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"two-qubit-adiabatic-emulation.json"</span>
 <span class=p>)</span>
@@ -425,12 +425,12 @@
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>):</span>
     <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>local_emulator</span><span class=p>()</span><span class=o>.</span><span class=n>run</span><span class=p>(</span><span class=mi>1000</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>emu_batch</span><span class=p>,</span> <span class=n>emu_filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3># get emulation batch, running 1000 shots per task emu_filename = os.path.join( os.path.abspath(""), "data", "two-qubit-adiabatic-emulation.json" ) if not os.path.isfile(emu_filename): emu_batch = batch.braket.local_emulator().run(1000) save(emu_batch, emu_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9c24f6aa"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Then, we can run the program on the hardware after parallelizing the tasks. We can then save the results to a file.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=d2ccfb9b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"two-qubit-adiabatic-job.json"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3># get emulation batch, running 1000 shots per task emu_filename = os.path.join( os.path.abspath(""), "data", "two-qubit-adiabatic-emulation.json" ) if not os.path.isfile(emu_filename): emu_batch = batch.braket.local_emulator().run(1000) save(emu_batch, emu_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=77f0bece"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Then, we can run the program on the hardware after parallelizing the tasks. We can then save the results to a file.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b2e72bd3"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"two-qubit-adiabatic-job.json"</span><span class=p>)</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>filename</span><span class=p>):</span>
     <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>100</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>hardware_batch</span><span class=p>,</span> <span class=n>filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4> filename = os.path.join(os.path.abspath(""), "data", "two-qubit-adiabatic-job.json") if not os.path.isfile(filename): hardware_batch = batch.parallelize(24).braket.aquila().run_async(shots=100) save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=aa4d8a2d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plot-the-results>Plot the Results<a class=anchor-link href=#plot-the-results>¶</a></h2><p>To show the blockade effect on the system, we will plot the probability of having <code>0</code>, <code>1</code>, or <code>2</code> Rydberg atoms as a function of time. We will do this for both the emulator and the hardware. We can use the following function to get the probabilities from the shot counts of each of the different configurations of the two Rydberg atoms: <code>00</code>, <code>10</code>, <code>01</code>, and <code>11</code>. Note that <code>0</code> corresponds to the Rydberg state while <code>1</code> corresponds to the ground state. As such, <code>00</code> corresponds to two Rydberg atoms, <code>10</code> and <code>01</code> corresponds to one Rydberg atom and one ground-state atom, and <code>11</code> corresponds to two ground-state atoms.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=58b86505"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>rydberg_state_probabilities</span><span class=p>(</span><span class=n>emu_counts</span><span class=p>):</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4> filename = os.path.join(os.path.abspath(""), "data", "two-qubit-adiabatic-job.json") if not os.path.isfile(filename): hardware_batch = batch.parallelize(24).braket.aquila().run_async(shots=100) save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b7ca1ab7"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plot-the-results>Plot the Results<a class=anchor-link href=#plot-the-results>¶</a></h2><p>To show the blockade effect on the system, we will plot the probability of having <code>0</code>, <code>1</code>, or <code>2</code> Rydberg atoms as a function of time. We will do this for both the emulator and the hardware. We can use the following function to get the probabilities from the shot counts of each of the different configurations of the two Rydberg atoms: <code>00</code>, <code>10</code>, <code>01</code>, and <code>11</code>. Note that <code>0</code> corresponds to the Rydberg state while <code>1</code> corresponds to the ground state. As such, <code>00</code> corresponds to two Rydberg atoms, <code>10</code> and <code>01</code> corresponds to one Rydberg atom and one ground-state atom, and <code>11</code> corresponds to two ground-state atoms.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9f0b7a9d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>rydberg_state_probabilities</span><span class=p>(</span><span class=n>emu_counts</span><span class=p>):</span>
     <span class=n>probabilities_dict</span> <span class=o>=</span> <span class=p>{</span><span class=s2>"0"</span><span class=p>:</span> <span class=p>[],</span> <span class=s2>"1"</span><span class=p>:</span> <span class=p>[],</span> <span class=s2>"2"</span><span class=p>:</span> <span class=p>[]}</span>
 
     <span class=c1># iterate over each of the task results</span>
@@ -445,14 +445,14 @@
         <span class=n>probabilities_dict</span><span class=p>[</span><span class=s2>"2"</span><span class=p>]</span><span class=o>.</span><span class=n>append</span><span class=p>(</span><span class=n>task_result</span><span class=o>.</span><span class=n>get</span><span class=p>(</span><span class=s2>"00"</span><span class=p>,</span> <span class=mi>0</span><span class=p>)</span> <span class=o>/</span> <span class=n>total_shots</span><span class=p>)</span>
 
     <span class=k>return</span> <span class=n>probabilities_dict</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5>def rydberg_state_probabilities(emu_counts): probabilities_dict = {"0": [], "1": [], "2": []} # iterate over each of the task results for task_result in emu_counts: # get total number of shots total_shots = sum(task_result.values()) # get probability of each state probabilities_dict["0"].append(task_result.get("11", 0) / total_shots) probabilities_dict["1"].append( (task_result.get("10", 0) + task_result.get("01", 0)) / total_shots ) probabilities_dict["2"].append(task_result.get("00", 0) / total_shots) return probabilities_dict</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f8049dbc"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Before we can plot the results we need to load the data from the files.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ec685996"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># get emulation report and number of shots per each state</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5>def rydberg_state_probabilities(emu_counts): probabilities_dict = {"0": [], "1": [], "2": []} # iterate over each of the task results for task_result in emu_counts: # get total number of shots total_shots = sum(task_result.values()) # get probability of each state probabilities_dict["0"].append(task_result.get("11", 0) / total_shots) probabilities_dict["1"].append( (task_result.get("10", 0) + task_result.get("01", 0)) / total_shots ) probabilities_dict["2"].append(task_result.get("00", 0) / total_shots) return probabilities_dict</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=91c54505"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Before we can plot the results we need to load the data from the files.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=83ecc9aa"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># get emulation report and number of shots per each state</span>
 <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 
 <span class=c1># get hardware report and number of shots per each state</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>filename</span><span class=p>)</span>
 <span class=c1># hardware_batch.fetch()</span>
 <span class=c1># save(hardware_batch, filename)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6> # get emulation report and number of shots per each state emu_batch = load(emu_filename) # get hardware report and number of shots per each state hardware_batch = load(filename) # hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6af7087d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>We can use the <code>rydberg_state_probabilities</code> function to extract the probabilities from the counts. This function takes a list of counts and returns a dictionary of probabilities for each state. The counts are obtained from the <code>report</code> of the <code>batch</code> object.</p> <p>Now, we can plot the results!</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=a11d26af"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-6> # get emulation report and number of shots per each state emu_batch = load(emu_filename) # get hardware report and number of shots per each state hardware_batch = load(filename) # hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=cbb81410"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>We can use the <code>rydberg_state_probabilities</code> function to extract the probabilities from the counts. This function takes a list of counts and returns a dictionary of probabilities for each state. The counts are obtained from the <code>report</code> of the <code>batch</code> object.</p> <p>Now, we can plot the results!</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=c2189be8"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 
 <span class=n>emu_rydberg_state_probabilities</span> <span class=o>=</span> <span class=n>rydberg_state_probabilities</span><span class=p>(</span><span class=n>emu_report</span><span class=o>.</span><span class=n>counts</span><span class=p>())</span>
@@ -488,4 +488,4 @@
 <span class=n>ax</span><span class=o>.</span><span class=n>set_xlabel</span><span class=p>(</span><span class=s2>"time ($\mu s$)"</span><span class=p>)</span>
 <span class=n>ax</span><span class=o>.</span><span class=n>set_ylabel</span><span class=p>(</span><span class=s2>"Probability"</span><span class=p>)</span>
 <span class=n>fig</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-7> emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_rydberg_state_probabilities = rydberg_state_probabilities(emu_report.counts()) hw_rydberg_state_probabilities = rydberg_state_probabilities(hardware_report.counts()) emu_distances = emu_report.list_param("atom_distance") hw_distances = hardware_report.list_param("atom_distance") fig, ax = plt.subplots() emu_colors = ["#55DE79", "#EDFF1A", "#C2477F"] # Green, Yellow, Red emu_lines = [] hw_lines = [] for rydberg_state, color in zip(["0", "1", "2"], emu_colors): (hw_line,) = ax.plot( emu_distances, hw_rydberg_state_probabilities[rydberg_state], label=rydberg_state + "-Rydberg QPU", color=color, ) (emu_line,) = ax.plot( hw_distances, emu_rydberg_state_probabilities[rydberg_state], color="#878787", label="Emulator", ) emu_lines.append(emu_line) hw_lines.append(hw_line) ax.legend(handles=[*hw_lines, emu_lines[-1]]) ax.set_xlabel("time ($\mu s$)") ax.set_ylabel("Probability") fig.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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+</pre></div> <div class=clipboard-copy-txt id=cell-7> emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_rydberg_state_probabilities = rydberg_state_probabilities(emu_report.counts()) hw_rydberg_state_probabilities = rydberg_state_probabilities(hardware_report.counts()) emu_distances = emu_report.list_param("atom_distance") hw_distances = hardware_report.list_param("atom_distance") fig, ax = plt.subplots() emu_colors = ["#55DE79", "#EDFF1A", "#C2477F"] # Green, Yellow, Red emu_lines = [] hw_lines = [] for rydberg_state, color in zip(["0", "1", "2"], emu_colors): (hw_line,) = ax.plot( emu_distances, hw_rydberg_state_probabilities[rydberg_state], label=rydberg_state + "-Rydberg QPU", color=color, ) (emu_line,) = ax.plot( hw_distances, emu_rydberg_state_probabilities[rydberg_state], color="#878787", label="Emulator", ) emu_lines.append(emu_line) hw_lines.append(hw_line) ax.legend(handles=[*hw_lines, emu_lines[-1]]) ax.set_xlabel("time ($\mu s$)") ax.set_ylabel("Probability") fig.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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Cz2aQkvA3LzMxk+fLl5ObmAhAVFcWUKVPo1q2bj1vW8iTcCCFEBVfImYKeydhYRjkv4iAZMy9j4R1M3IaRW9HQttbIqktERATBwcEUFRWRkZFB9+7dfd0k0UDFxcWsWbOG/fv3A2AymRg7diyDBw/usDvES7gRQogqXCHnEvRMxcYPlPMSDg5SzguYeRsTt2HiVhTafje/uyR89+7dpKamSrhpQxwOB9u3b2fjxo1Yra6tewYPHszYsWPx9+/Yi7JKuBFCiFooaDBwGXqmYeVbynkZJ4cp5znMvImJOzAxG4UAXzf1nFQON6JtSEtLY8WKFeTl5QEQExPDlClT6Ny54y5MWZmEGyGEOAsFDUYux8BlWPmmIuSkUM4CzPy3IuTMoq1uYRIfH+8pCc/Ly2u1y+8LKCgoYPXq1Rw65Fpp3t/fnwkTJtC/f/92s7pwU5BwI4QQ9aSgxcgfMPB7rHxFOS/hJJ1ynqoUcm5Gwc/XTW0Qo9FIbGwsR48eJTU1VcJNK2Sz2fjll1/YsmULdrsdRVEYNmwYY8aMwWRquyuINxcJN0II0UCukHMVBi7HyheU8wpOjlLOE5hZiB93YuSPbSrkJCUlecKNlIS3HqqqcvjwYVatWkVhYSEAcXFxTJ48mcjIjrWidkNIuBFCiEZS0GHkagz8ASufV4ScTMp4lHLewI95GLm+TezN5S4JP3r0qJSEtxKnT59m5cqVpKWlARAUFMTEiRPp06ePDEGdhYQbIYQ4Rwp6jFyLgSuxsBQz/8LJccp4mHJew4+/YOTaigUmWycpCW89rFYrmzZtYuvWrTidTrRaLSNGjGDUqFEYDM2/T197IOFGCCGaiIIBEzdgZCYWPsbMv3GSRRl/x8x/MHE3Rq5ukY1kG6pySXhKSoqEGx9QVZXk5GRWr15NSUkJ4FpBetKkSW1q3y+n1YbqVNGafPdzLuFGCCGamCvk3ISRa7DwEeW8ipMTlHE/Zl7Fj3swMAOF1jX0U7kkXFVVGfpoQTk5OaxYsYLMzEwAQkNDmTRpEj169PBxy+pPVVXythwk9c2fiLigH4m3TvZZWyTcCCFEM1EwYmIWRq7FwgeU8x+cHKOUv1LOq/hxNwauQmklv4rdu4QXFBSQn58vVVMtwGw2s2HDBnbu3Imqquh0OkaNGsXIkSPR6VrHz0V9lGXmkrpwGfk7UgDIXf8b8TeOR2PwTYBvO985IYRooxRMmLgVI9dj4X3KeQ0nRyllPuX8u6In5w8+DzkGg8FTEp6SkiLhphmpqsqvv/7KunXrKCsrA6B3795MnDiRkJC2s4eZvdRMxgfrOPH1L6gOJ4peS7crRxF77YU+CzYg4UYIIVqMgl/F/lR/xMx7mHkdJ+mUck9FyPk/DFyOgtZnbaxcEj58+HCftaM9y8rKYvny5WRlZQHQqVMnJk+eTEJCgm8b1gCq08nJlXtIX7QSW0EpAOEje5H0p6n4xXTycesk3AghRItT8MeP2zFxI2aWYOYNnKRSyl2U8y/8mI+B6Si0/KaH3bt3l13Cm0lZWRlr167l119/BVw9ZRdccAFDhw5Fq/VdoG2oogPHSHn9B0oOnQDAr1snkm6/mPBhPX3csjMk3AghhI8oBODHnZi4GTOLMPNfnByhlD9j5l/48X/oubRFQ06nTp08JeFHjx5tUxNaWyun08muXbtYv349FosFgP79+zN+/HgCA9vO5qvW08WkLV5Jzso9AGj9DcTdMJ6Y349Ao29dcaJ1tUYIIToghUD8+AsmZmPmbcy8hYODlHA7Wvrix3z0XNwiIUdRFLp3786uXbtITU2VcHOOMjIyWLFiBbm5uQBERUUxZcoUunXr5uOW1Z/TZufEV7+Q8eE6HOWu3cejpwwmYdZFGMKDfNy6mkm4EUKIVkIhCD/+DyO3YOEdzLyJg2RKmIuW8/DjXvRMQaF5S7STkpLYtWsXKSkpUhLeSMXFxaxZs4b9+/cDYDKZGDt2LIMHD0ajafnhxsbK23qI1P/+RPnx0wAE9elK9zsuIah36w5nEm6EEKKV0RCCH/Mxcgtm3sLM2zj4jRJuQcvAip6cSc0WcuLj49FqtRQWFpKXl0enTr6fINpWOBwOtm3bxsaNG7HZbAAMHjyYsWPH4u/fdnaNLz9+mtT/LiNv62EA9GEBJN4ymaiLBqK0gXAm4UYIIVopDaH48zdM3IqZNzHzDg5+pYRZaBlc0ZMzoclDjrskPD09ndTUVAk39ZSamsrKlSvJy8sDICYmhilTptC5c2cft6z+7GUWMj9ez/EvNqPanSg6DTGX/46468eiC2j9e6S5SbgRQohWTkM4/jyAidswsxAzi3CwmxJuRMsQ/PkbOsY2achJSkoiPT2dlJQUKQk/i4KCAlatWsXhw65eDn9/fyZMmED//v3bzJCe6nSSs3ov6YtWYM1zbf0QNqwHSX+6GP/YCB+3ruEk3AghRBvhCjl/rwg5b2BmCQ52Ucz16BiGH39FxwVNEnK6d+/O6tWryczMxGq1yoaNNbDZbPzyyy9s2bIFu92OoigMGzaMMWPGYDK1nV6O4kPHSXnjR4qTjwFgigkn6U9TCR/Rq82Es6ok3AghRBujIQJ/HsbEnyjndSy8j53tFHMtOn5XMVw1+pzeIzw8nJCQEAoLC8nIyJCqqUpUVeXw4cOsWrWKwsJCAOLi4pg8eTKRkZE+bl39WQtKSF+8ipPLd4EKGpOeuOvH0fWK36ExtO140LZbL4QQHZiGKAJ4FD/uoJzXsPA/7GyhmJnoGI0ff0XPyEZd271LuLtqSsKNy+nTp1m5ciVpaWkABAUFMXHiRPr06dNmejmcdgdZ32zl6P/W4ihzrbsTddFAEm6ZhLFTsI9b1zQk3AghRBunIZoAHq8IOf/BwofY2UQxV6LjwoqenIbPm6m83k1HLwm3WCxs2rSJbdu24XQ60Wq1jBgxglGjRrWpIbv8HUdI+e8yyjNOARDYswvd77iE4H5xPm5Z05JwI4QQ7YSGLgTwFCb+jJlXsfAxdjZQzAb0jMePe9Fxfr2vFxcXJyXhQF5eHh9++CElJa6Jtt27d2fSpEmEhYX5uGX1V56VR9qbP3F680EA9CH+JMy6iOgpQ1C0rb+0u6Ek3AghRDujpSsBPIOJeZj5NxY+wcZabKxFz8SKiceDznqdyiXhKSkpHTbcbNu2jZKSEkJCQpg8eXKbGqJzmK1kfryBY59vQrU5QKMQc/lI4m8Yhy7Qz9fNazbtL64JIYQAQEs3AniOEDZg4FpAi43VFDGNYm7Gzt6zXqN79+6Aaw2XjkhVVc/XPmnSpDYTbFRVJWfNXrbPeZXMjzeg2hyEDkni/DfuoPufLm7XwQak50YIIdo9LXEE8iIO7qKcV7DyOTZWYmMlgSzGwJRaX5uUlMSqVas6bEl4Xl4ehYWFaLVa4uPjfd2ceik5kkXKGz9S9FsGAMboUJL+NJVOo9rOpOdzJeFGCCE6CC0JBPIKDv5CGY9iYxVm3qwz3FQuCT969Cg9e/ZswRb7XkpKCgCxsbGtPtjZCktJf3c12T/ucJV2G/XEXnMBXa8ajdao93XzWpSEGyGE6GC0JBHAMxQwAjubcZCBlpqrZdy7hO/cuZPU1NQOF27cQ1JJSUk+bkntVIeDE99tJ+P9NdhLzABEjutP4pzJGCNDfNw635BwI4QQHZCGGHRciJ31WPkMP+bXem5SUhI7d+7scLuEW61WMjMzgTNzj1qbgt2ppCxcRll6DgABSdF0v+MSQgYk+LZhPibhRgghOigjM7GzHgtLMXEPSi01Ju5dwouKijh9+jQREW1vr6HGOHr0KA6Hg5CQEMLDw33dHC/m7HxS317O6Z+TAdAF+5Fw80Q6Xzy0XZZ2N5SEGyGE6KAMXEIpgTjJwM4v6BlV43l6vZ64uDjS0tJISUnpMOHGPSTVvXv3VtNb5TBbObZ0I8eWbsRptYNGoctlw4m/cTz6IH9fN6/VkHAjhBAdlIIfRn6PhQ+xsLTWcAOuoam0tDRSU1MZObJxWzq0JaqqeiYTt4b5NqqqcmrDftLeXo4lx7WfVcjABLrfcQkBidE+bl3r4/O+q9dee42EhARMJhMjR45k69atdZ7/yiuv0Lt3b/z8/IiNjeX//u//MJvNLdRaIYRoXwzMBMDKd6iU1Xqee86JuyS8vTt9+jRFRUVotVri4ny7NUFp2kn23v8uB55eiiWnEGNUCH3+MZMBz94swaYWPu25+eSTT5g/fz4LFy5k5MiRvPLKK0ydOpWDBw8SFRVV7fwPP/yQBx54gEWLFjF69GgOHTrErFmzUBSFl156yQdfgRBCtG06hqMhASfpWPkBIzNqPC8sLIzQ0FAKCgpIT0+nV69eLdzSluUekvJlCbituIyj760h6/vt4FTRGHR0mzmGbjPHoDW17rJ0X/Npz81LL73E3LlzmT17Nv369WPhwoX4+/uzaNGiGs/ftGkTY8aM4frrrychIYEpU6Zw3XXXnbW3RwghRM0UFE+gsfBp7edV7BIOHWO1Yl+WgKsOJye+28b2W14l69tt4FSJuKAfQ9+aR/yNEyTY1IPPwo3VamXHjh1MmjTpTGM0GiZNmsTmzZtrfM3o0aPZsWOHJ8ykpqbyww8/MG3atFrfx2KxUFRU5HUTQghxhntoys4mHByr9bzKWzGoqtoibfMFX5aAF+5NZ9dd/yXlP99jLy7HPyGKAc/eTN+HrsYUHdqibWnLfDYsderUKRwOB9HR3uOF0dHRHDhwoMbXXH/99Zw6dYoLLrgAVVWx2+3cfvvt/P3vf6/1fRYsWMBjjz3WpG0XQoj2REs3dIzBzsaKNW/uqfE89y7h7b0k3Bcl4JbcQtLeXkHuun0A6AJNxN80gS6XDkPRalukDe2JzycUN8TatWt5+umnef3119m5cydffPEF33//PU888UStr3nwwQcpLCz03NxpXAghxBnGit4bC0tRqblXxl0SDme2JWiPWrIE3Gm1kfHhOrbP+Y8r2CjQedpQhr1zFzG/HynBppF81nMTERGBVqvl5MmTXsdPnjxJ586da3zNww8/zI033sicOXMAGDBgAKWlpdx222384x//QKOpntWMRiNGo7HpvwAhhGhHDFxKKf/ASTp2tqNneI3nde/evV2XhLdUCbiqqpzedIC0t37CnF0AQPB5cXS/4xICe3RptvftKHzWc2MwGBg6dCirVq3yHHM6naxatYpRo2pea6GsrKxagNFWpNr2PP4rhBDNTcEfA5cCYK1jYrH7Az8zMxOLxdIibWtJLVECXno0h31/f5/kJz7BnF2AISKI3vdfxcAXZkuwaSI+LQWfP38+N998M8OGDWPEiBG88sorlJaWMnv2bABuuukmunbtyoIFCwCYPn06L730EkOGDGHkyJEcOXKEhx9+mOnTp3tCjhBCiMYxcjVWPsXKt/jzOAp+1c4JDw/3lIQfPXq03ZWEu3tt4uLimrwE3F5SztH/reXEN1vBqaLotXS7ajSx114oFVBNzKfh5pprriE3N5d//vOfZGdnM3jwYJYtW+aZZJyRkeHVU/PQQw+hKAoPPfQQx48fJzIykunTp/PUU0/56ksQQoh2Q8dINMTiJBMryzDyhxrP6969Ozt27CA1NbXdhZvmKAFXHU6yl+8ifckq7IWuhRI7je5D4twp+HVpXXtWtReK2sHGc4qKiggJCaGwsJDg4GBfN0cIIVqVMl7EzEvoGEswH9V4TkpKCkuXLiU4OJg77rij1ey7dK6sViv/+te/cDgczJ07l06dOp3zNYv2Z5Dy+o+UHMkCwC82gu63X0zY0B7nfO2OpiGf37K3lBBCCA8jMzDzEnY24OQEGmKqnRMXF4dOp6OoqIhTp04RGRnpg5Y2vaYsAbecLiL9nZXkrP4VAK2/kfgbx9Nl+gg0OplG0dwk3AghhPDQEo+O32FnCxY+x4+7qp3jLglPTU0lNTW13YSbpigBd1rtHP9yMxkfrcdptoEC0VOGkDD7IgyhgU3ZXFEHCTdCCCG8GLm6ItwsxcQ8FKp/0CclJZGamkpKSkq7KAk/1xJwVVXJ++UQqf9dhjkrH4Cgvt3ofsclBPXq2qRtFWcn4UYIIYSXM2vepOBgJzqGVjune/furFy5kmPHjmGxWNr8emKVS8Dj4+Mb9NqyzFOk/ncZ+duPAKAPCyTx1slETRyAUsP6a6L5SbgRQgjhRSEQA9Ow8jkWltYYbsLCwggLCyM/P5/09HR69+7tg5Y2ncol4Hq9vl6vsZeayfhwHSe++gXV4UTRaeh65Shirx2Lzr9th722TiKlEEKIaoxcDYCVr1Ex13hOe9olvCEl4KrTVdq9fc6rHP98M6rDSfiIngz9750k3jJZgk0rID03QgghqtExGg1dcXIcKz9h5PJq51Re70ZV1TZbEm6xWOq9C3jRgWOkvvEjxQePA+DXNZykP11M+Ij2td5PWyfhRgghRDUKGgzMwMy/sPJZjeEmNjYWnU5HcXExubm5REVF+aCl5+7o0aM4nU5CQ0MJCwur8RxrXjHpi1dxcsVuALR+BuKuH0fMFSPR6OWjtLWR/yJCCCFqZKwINzbW4iQbDd6bGuv1euLj40lJSSE1NbXNhpvKQ1I19T4VHz7B3vuX4CizAhA1aRCJsydh6BTUou0U9SdzboQQQtRISxI6hgNOLHxZ4zltfd6NqqpnnW9zbOlGHGVWArp3ZtArc+j91z9IsGnlJNwIIYSolYGZgGuncJXqu/W4A4G7JLytOVsJuL3UTN6WgwD0uuf3BPfp1tJNFI0g4UYIIUStDEwHTDg4hIM91Z4PCwsjPDwcp9NJenp6i7fvXJ2tBPzUxmScVjt+cREE9OjS0s0TjSThRgghRK00BGPgEgAsLK3xnLY8NHW2ISn33lBREwe22WqwjkjCjRBCiDoZPUNTX6FSfeipcrhR1epDV63V2UrALaeKKNyTBkDU+AEt2jZxbiTcCCGEqJOOC1DojEoBNlZWe969S7i7JLytOFsJeO7avaBCcP84TJ1rLhEXrZOEGyGEEHVS0GJkBgAWPq32vE6n80zGbUtDU2crAfcMSU0Y2KLtEudOwo0QQoizcg9N2ViDk+q9M+6hKfcE3daucgl4TUNSpWknKU09iaLTEDG2X0s3T5wjCTdCCCHOSksPtAwBHFj4otrz7oBw7NgxzOaa96JqTU6dOuUpAY+Li6v2fM4aV69N+Ihe6IP8W7p54hxJuBFCCFEvZzbTXFptzZvQ0FDCw8NRVbVNlIS7e21qKgFXnU5y1+wFXFVSou2RcCOEEKJeDPweMOIgGQe/VXu+LZWE11UCXrgvA0tuEdoAI+EjerZ000QTkHAjhBCiXjSEYmAKUPPEYvfQVGsvCT9bCXhuxUTiiAv6oTFUX9hPtH4SboQQQtSbwTM09SUqVq/nYmNj0ev1lJSUkJOT44vm1UtdJeBOq43cDa5eKRmSarsk3AghhKg3PWNRiEIlDxurvZ7T6XSeybmteWiqrhLwvK2HcZRaMEQEEzKg+l5Tom2QcCOEEKLeFHQYuQqoe2iqtZaEn60E/Mx2CwNQNPIR2VbJfzkhhBANYvCsebMKJ6e9nnNP0D1+/HirLAmvqwTcVlxG3rbDgAxJtXUSboQQQjSIjt5oGQTYsfKl13OhoaF06tSp1ZaEu3tt4uPjq5WAn9qwH9XmICAxmoCEaF80TzQRCTdCCCEazL3mTU07hbfmknD3cFlNJeCVdwAXbZuEGyGEEA3mWvNGj4N92Nnv9Vxr3SXcYrFw7NgxoHq4MWfnU7QvAxSIHN/fF80TTUjCjRBCiAbTEI6eyYBrxeLKWmtJeOUS8PDwcK/ncta6ViQOGZSIMTLEF80TTUjCjRBCiEZxb6Zp4QtUbJ7jrXWX8NpWJVZVVYak2hkJN0IIIRpFzwQUIlA5hY01Xs+1tl3CVVX1tKVqCXhpSjblGafQGHREjOnri+aJJibhRgghRKMo6DFyJVB9YnFrKwk/deoUxcXFXgsNurl7bcJH9kIXYPJF80QTk3AjhBCi0c6sebMCJ3me462tJLy2XcBVh5Mc2QG83ZFwI4QQotF09ENLf8CGlW+8nmtNQ1O1lYAX7EnDll+CLsiPsGE9fNE00Qwk3AghhDgnZyYWe2/H0Fp2Ca+rBNw9JBU57jw0el2Lt000Dwk3QgghzomBPwA6HOzBzkHP8W7duqHX6yktLeXkyZM+a196ejpOp5OwsDCvEnCH2crpjcmADEm1NxJuhBBCnBMNndAzCfBe86a1lITXVgJ+estBHOVWTJ1DCeob64umiWYi4UYIIcQ5OzM09Tkqds/xykNTvlB5F/Cq4SbXPSQ1YSCKorR420TzkXAjhBDinOmZiEI4KjnYWO857uuS8NpKwK0FpeRtPwJA1MQBLd4u0bwk3AghhDhnCoaKuTdgrTSxOCQkhIiICFRVJS0trcXbVVsJ+Kn1+8CpEtgzBv/YyBZvl2heEm6EEEI0CfdO4VaW46TAc9yXJeG1lYDnrK5Y2+YimUjcHkm4EUII0SS0nIeWvoDFa80bd7BIS0tr0ZLwyiXglbdcKD9xmuIDx0CjEDn2vBZrj2g5Em6EEEI0CQXFs2Jx5TVvYmNjMRgMLV4SXrkEPCwszHPc3WsTNiQJQ3hQi7VHtBwJN0IIIZqMa68pLQ524cA1YVer1XpKwltyaKqmKinZAbxjkHAjhBCiyWiIRM8EwHszTXfAaKmS8Mol4JWHpIoPHsd8Ig+NUU+n0X1apC2i5Um4EUII0aTcE4stfIaKAzgTbk6cOEF5eXmztyE3N9dTAh4be2aBPvfaNp1G90HrZ2z2dgjfkHAjhBCiSemZhEIoKtnY+Rlo+ZLwmkrAnXYHuev2ATIk1d5JuBFCCNGkFIwYuALwnljckkNTNc23KdiZgq2wDH2IP2HnJ9X2UtEOSLgRQgjR5NzbMVhZhpMioOV2Ca+tBNyzA/j4AShabbO9v/A9CTdCCCGanJZBaOkFmLHyLeDaJdxgMFBWVtasJeE1lYDbyyyc3nwAkCGpjkDn6wa0Vg6HA5vN5utmiHbKYDCg0cjfFqL9cq15czXlPImVpZi4wVMSfvjwYVJSUujcuXOzvHdNQ1KnNx3AabHj17UTgb1imuV9Resh4aYKVVXJzs6moKDA100R7ZhGoyExMRGDweDrpgjRbIxcSTlPY2cbDlLRkkT37t05fPgwqampjBkzpsnfs7YScM+Q1MQBsgN4ByDhpgp3sImKisLf31/+JxBNzul0cuLECbKysoiLi5OfMdFuaYhGz3hsrMbCZ/hzX7WScD8/vyZ9z5pKwK2niynY7Qo8URNkSKojkHBTicPh8ASbTp06+bo5oh2LjIzkxIkT2O12r52KhWhvDMzAxmqsLMWPvxIcHExkZCS5ubmkpaXRr1+/Jn2/mkrAc9e5dgAP6tsNv5jwJn0/0TrJoH8l7jk2/v7+Pm6JaO/cw1EOh8PHLRGieRmYikIwTk5gZyPQvLuEu69Z05CUTCTuOCTc1ECGCURzk58x0VEomDBwOXBmO4bm2iXcYrFw/Phxr/coy8il5EgWilYjO4B3IBJuhBBCNKsza978gEqJV0l4dnZ2k71PTSXg7l6bsGE90IcENNl7idbN5+HmtddeIyEhAZPJxMiRI9m6dWud5xcUFHDnnXfSpUsXjEYjvXr14ocffmih1or6UhSFr776CnD9wlEUhd27d/u0TUII39ByPhq6A+VY+R6tVktCQgLQtENTVUvAVaeTnDV7ARmS6mh8Gm4++eQT5s+fzyOPPMLOnTsZNGgQU6dOJScnp8bzrVYrkydPJj09nc8++4yDBw/y1ltv0bVr1xZueevU0KAIrhDivgUHBzN8+HC+/vrrFmit723atIlp06YRFhaGyWRiwIABvPTSS9XmwVT+HoWEhDBmzBhWr17teX78+PHcc8891a6/ZMkSQkNDm/mrEKL1U1A8vTfu7Rgqr1bcFGoqAS/an4nlZAFafwPhI3s1yfuItsGn4eall15i7ty5zJ49m379+rFw4UL8/f1ZtGhRjecvWrSIvLw8vvrqK8aMGUNCQgLjxo1j0KBBLdzy1qehQbGyxYsXk5WVxfbt2xkzZgwzZsxg7969LdDqc2O1Whv92i+//JJx48bRrVs31qxZw4EDB7j77rt58sknufbaa6vNA3B/jzZu3EhERASXXXZZi+yPI0R7YeQqQMHOFhwcJTExEXCVhJeVlZ3z9WsqAXcPSUWM6YfWJGtKdSQ+CzdWq5UdO3YwadKkM43RaJg0aRKbN2+u8TXffPMNo0aN4s477yQ6Opr+/fvz9NNP11lxYrFYKCoq8rq1Rw0NipWFhobSuXNnevXqxRNPPIHdbmfNmjWoqkqPHj144YUXvM7fvXs3iqJw5MgRAA4fPszYsWMxmUz069ePFStW1Pg+Bw4cYPTo0ZhMJvr378+6deu8nt+3bx+XXHIJgYGBREdHc+ONN3Lq1CnP8+PHj2fevHncc889REREMHXqVMD1c9GzZ09MJhMTJkzg3XffRVGUWhdiLC0tZe7cufz+97/nzTffZPDgwSQkJDBnzhzeffddPvvsMz799FOv17i/R/379+eNN96gvLy81q9TCFGdhhh0XAiAhc88JeFAk+wS7v5jIz4+Hr1ej9Nm59T63wCIlCGpDsdn4ebUqVM4HA6io6O9jkdHR9c6wSw1NZXPPvsMh8PBDz/8wMMPP8yLL77Ik08+Wev7LFiwgJCQEM/NnejrS1VVzE6rT271rSJoTFCsid1u55133gFcpcqKonDLLbewePFir/MWL17M2LFj6dGjB06nkyuvvBKDwcAvv/zCwoULuf/++2u8/t/+9jfuvfdedu3axahRo5g+fTqnT58GXHOpJk6cyJAhQ9i+fTvLli3j5MmTXH311V7XePfddzEYDGzcuJGFCxeSlpbGjBkzuOKKK9izZw9/+tOf+Mc//lHn17l8+XJOnz7NX//612rPTZ8+nV69evHRRx/V+nr3omPn0nMkREdkxPX/s5WlqDibdGjKPXfHPd8mf/sR7CVmDJ2CCB2YcM7XF21LoxbxW7NmDRMmTGjqtpyV0+kkKiqKN998E61Wy9ChQzl+/DjPP/88jzzySI2vefDBB5k/f77ncVFRUYMCjkW1cdPRR8+16Y3yXvyjmJSzd6XWFRQPHDhw1tdfd911aLVaysvLcTqdJCQkeELFrFmz+Oc//8nWrVsZMWIENpuNDz/80NObs3LlSg4cOMBPP/1ETIxrv5ann36aSy65pNr7zJs3j6uuugqAN954g2XLlvHOO+9w33338Z///IchQ4bw9NNPe85ftGgRsbGxHDp0iF69XOPlPXv25LnnnvOc88ADD9C7d2+ef/55AHr37s2+fft46qmnav16Dx06BEDfvn1rfL5Pnz6ec6oqKyvjoYceQqvVMm7cuFrfQwhRnYGLKSMIJ5nY+YWkpCS2bNni2SW8sUsk1FQCnrPKvQN4fxStz2tnRAtr1H/xiy++mO7du/Pkk0+SmZnZqDeOiIhAq9VW2xn25MmTtW6m1qVLF3r16oW20lb1ffv2JTs7u9a/oo1GI8HBwV63juj2228nMDDQc6vs5ZdfZvfu3fz444/069ePt99+m/Bw1yqeMTExXHrppZ7hrW+//RaLxcLMma7JgcnJycTGxnqCDcCoUaNqbEPl4zqdjmHDhpGcnAzAnj17WLNmjVcb+/TpA3hXUwwdOtTrmgcPHmT48OFex0aMGFGv70ldPWNV93y67rrrCAwMJCgoiM8//5x33nmHgQOlq1uIhlDww8B0wDWxuGvXrhiNRsrLy8nKymr0dd0l4OHh4YSFhWEvKef0LwcBqZLqqBrVc3P8+HHef/993n33XR577DEmTpzIrbfeyhVXXFHvjQANBgNDhw5l1apVXHHFFYCrZ2bVqlXMmzevxteMGTOGDz/8EKfT6dlR+dChQ3Tp0qXZNiA0Knrei3+0Wa5dn/euj/oExccff7zGYRiAzp0706NHD3r06MHixYuZNm0a+/fvJyoqCoA5c+Zw44038vLLL7N48WKuueaaJl/FuaSkhOnTp/Pss89We65Lly6e+wEB575ORc+ePQFXMBs9enS155OTkxk8eLDXsZdffplJkyYREhLimSfgFhwcTGFhYbXrFBQUEBIScs7tFaI9MXA1Fj7EyncEaJ8kISGBgwcPkpqa6vVHUkNUHZI69XMyqs2Bf3wkAUnNs/O4aN0a1XMTERHB//3f/7F7925++eUXevXqxZ///GdiYmL4y1/+wp49e+p1nfnz5/PWW2/x7rvvkpyczB133EFpaSmzZ88G4KabbuLBBx/0nH/HHXeQl5fH3XffzaFDh/j+++95+umnufPOOxvzZdSLoiiYNAaf3OrbRVs5KLq5g6K7tyQqKsoTYHr06FHrtUaMGMHQoUO9hnWmTZtGQECAZyjplltu8TzXt29fMjMzvf7q2rJlS43XrnzcbrezY8cOz9DQ+eefz2+//UZCQoJXO3v06FFnoOnduzfbt2/3OrZt27ZazweYOnUq4eHhvPjii9We++abbzh8+DCzZs3yOu4OgFWDjbsNO3furHZ8586dnuE0IYSLjmFoSADKsPKDJ5A0dt6NqqqeCcmeIalK2y3IauAdlNoEjh8/rj7yyCOq0WhUAwICVK1Wq15wwQXqvn37zvraV199VY2Li1MNBoM6YsQIdcuWLZ7nxo0bp958881e52/atEkdOXKkajQa1aSkJPWpp55S7XZ7vdtaWFioAmphYWG158rLy9X9+/er5eXl9b5ea/Hxxx+rRqNRXbJkibp//371tttuU0NDQ9Xs7Ow6XweoX375pdexH374QTUajeqxY8c8x/7+97+rBoNB7du3r9e5DodD7devnzp58mR19+7d6vr169WhQ4d6XTctLU0F1Li4OPWLL75Qk5OT1dtuu00NDAxUc3NzVVV1/QxFRkaqM2bMULdu3aoeOXJEXbZsmTpr1izPf99x48apd999t9f7p6amqnq9Xr3vvvvUgwcPqp988onarVs3FVALCgpq/bqXLl2qarVade7cueqePXvUtLQ09e2331bDwsLUuXPnnvV7VFlKSopqMpnUu+66S92zZ4964MAB9cUXX1R1Op36448/1viatvyzJsS5KlNfVk+rMWqhOkMtKipSFyxYoC5YsEAtLS1t8LVOnjypLliwQH3++edVm82mmnMK1PUXP6Kun/qIWn4yv+kbL3ymrs/vqhodbqxWq7p06VL1kksuUXU6nfq73/1Ofeutt9SSkhI1LS1NveGGG6p9ELYG7TXcqGrdQbE2NX1wO51OtU+fPuodd9zhOZaSkqIC6nPPPVftGgcPHlQvuOAC1WAwqL169VKXLVtWY7j58MMP1REjRqgGg0Ht16+funr1aq/rHDp0SP3DH/6ghoaGqn5+fmqfPn3Ue+65R3U6naqq1hxuVFVVv/76a7VHjx6q0WhUx48fr77xxhsqcNb/juvXr1enTp2qBgcHq4AKqM8++2y9vkdVbd26VZ08ebIaGRmphoSEqCNHjqzzNW39Z02Ic2FXj6mn1a7qaTVGtauZ6ttvv60uWLCgXn8QV7V582Z1wYIF6qeffqqqqqpmfLpBXT/1EXX3Xxc1dbOFjzUk3Ciq2vBdy+666y4++ugjVFXlxhtvZM6cOfTv39/rnOzsbGJiYnA6nY3tVGoWRUVFhISEUFhYWG1ysdlsJi0tjcTEREwmk49a2Dpt2LCBiy66iMzMzGpVWa3NU089xcKFCxs02d1sNnP55ZeTmZnJunXrahx+akrysyY6uiKuxs5G/Pgrv6wdwpYtWzjvvPOYPn16g67zwQcfkJmZyeTJkxk6dCg7bn+dsvQcetw9nS6XDD37BUSbUdfnd1WNmnOzf/9+Xn31VU6cOMErr7xSLdiAa17OmjVrGnN50YpYLBaOHTvGo48+ysyZM1tlsHn99dfZtm0bqampvP/++zz//PPcfPPNDbqGyWTi66+/5qabbmL9+vXN1FIhhJt7zRsLn3nNu2nIH8Rms5ljx44Bri0XSlOzKUvPQdFribigX9M3WrQZjQo3jzzyCDNnzsRoNHodt9vtng8GnU4n64C0Ax999BHx8fEUFBR4rS/Tmhw+fJjLL7+cfv368cQTT3Dvvffy6KOPNvg6JpOJBx54wLMWjxCi+RiYBgTgJJ2oric8JeEN2SU8PT0dVVUJDw8nNDTUM5E4fEQv9EF+zdRy0RY0KtxMmDCBvLy8ascLCwt9srifaD6zZs3C4XCwY8eOVrtB6csvv8yJEycwm80cOnSIhx9+GJ2uUascCCFaiII/Bi4DwKH9rFG7hFfeBVx1OslZKzuAC5dGhRu1lpUkT58+3STrkAghhGj/zuwU/i1JSa6V4+tbEq5WKQEv3HsU66lidIEmwof3bJ4GizajQX/eXnnllYBr7ZdZs2Z5DUs5HA5+/fXXGhdFE0IIIarSMRINcTjJoFuSK9RkZWVRVlZ21oVCK+8CHhcXR+q/vwcg4sJ+aAzSc9vRNajnxr35pKqqBAUFeW1I2blzZ2677Tb+97//NVdbhRBCtCMKGk/vjT7oS8+q6PXpvXEPX8XHx6M4VE79vB+QISnh0qB4694dOiEhgb/+9a8yBCWEEOKcGJhBOS9iZwOJ3W8gJyeH1NTUGqtwK6s83ybvl0M4yiwYo0IIPi+uJZotWrlGV0tJsBFCCHGutMShYxSgEpt0BDh7SXjVEvDcNRXbLUwYgKKRHcBFA3puzj//fFatWkVYWBhDhgypc7+OmvbZEUIIIWpiZCZ2NhPe9QuMxj9gNpvJysqqtUKzcgl4gMZA3rbDAEROkCEp4VLvcHP55Zd7JhC7d/EWojYJCQncc8893HPPPYBrEvqXX34pPztCiGoMXEop/wBNCvGJIRw64Bqaqi3cVB6SOrXhN1S7k4DunQlIiGrJZotWrN7h5pFHHqnxvmgd1q9fz/PPP8+OHTvIysqqd5BISEjg6NGjAPj5+dG9e3fuvvtu5syZ08wt9r3ffvuNxx57jDVr1lBUVER8fDzXXnstDzzwgFelRuXvkb+/P7179+bBBx9k5kzXRMhZs2ZRUFDAV1995XX9tWvXMmHCBPLz8wkNDW2pL0uINkchEAPTsPI5sUlpHDoQQEpKChdeeGG1c1VV9YSb7t27k/PqakAmEgtvMjjZTpSWljJo0CBee+21Br/28ccfJysri3379vHHP/6RuXPn8uOPPzZDK5uW1Wpt9Gu3bNnCyJEjsVqtfP/99xw6dIinnnqKJUuWMHny5GrXdn+Pdu3axfDhw7nmmmvYtGnTuX4JQogK7u0YuiS5Srqzs7MpLS2tdl5OTg4lJSXo9Xoi9YEU7c8EBSLH1z0BWXQs9Q43YWFhhIeH1+smWt4ll1zCk08+yR/+8IcGvzYoKIjOnTuTlJTE/fffT3h4OCtWrABg4sSJzJs3z+v83NxcDAYDq1atAly/bKZPn46fnx+JiYl88MEHNb5PVlYWl1xyCX5+fiQlJfHZZ595PZ+ZmcnVV19NaGgo4eHhXH755aSnp3uenzVrFldccQVPPfUUMTEx9O7dG4BNmzYxePBgTCYTw4YN46uvvkJRFHbv3l1jO1RV5dZbb6Vv37588cUXjBgxgvj4eGbOnMm3337L5s2befnll2v8HvXq1YvXXnsNPz8/vv3223p/j4UQddMxGg1d8Q88SVS0awqEe5G+yty9NnFxceRtSAYgdHASxk51b6QoOpZ6D0u98sorzdiM1ktFBcp99O5+KNQ+cbupOZ1OvvzyS/Lz8zEYDADMmTOHefPm8eKLL3rmXP3vf/+ja9euTJw4EXCFjhMnTrBmzRr0ej1/+ctfyMnJqXb9hx9+mGeeeYZ//etfvP/++1x77bXs3buXvn37YrPZmDp1KqNGjWLDhg3odDqefPJJLr74Yn799VdPe1atWkVwcLAnfBUVFTF9+nSmTZvGhx9+yNGjRz3zfGqze/du9u/fz4cffoimSmXFoEGDmDRpEh999BH3339/ja/X6XTo9fpz6jkSQnhT0GBgBmb+RWxSJjkno0hJSalWEl55vk3Ows2Aq0pKiMrqHW4austy+1FOPr5ZyjuMw0Ddq3Q2hfvvv5+HHnoIi8WC3W4nPDzcM+fmyiuvZN68eXz99ddcfbWr23jJkiXMmjULRVE4dOgQP/74I1u3bmX48OEAvPPOO/Tt27fa+8ycOdNz3SeeeIIVK1bw6quv8vrrr/PJJ5/gdDp5++23PZV4ixcvJjQ0lLVr1zJlyhQAAgICePvttz1hZ+HChSiKwltvvYXJZKJfv34cP36cuXPn1vr1Hjp0CKDGNrqP//zzzzU+Z7VaefHFFyksLPSEOyFE0zAyEzP/omvSz+zYfCVpaWk4nU7PHyGVS8A7awNJzTyFxqCj05ia/18WHVe9h6WKioq87td1E63P008/TWBgoOeWkZHhee5vf/sbu3fvZvXq1YwcOZKXX36ZHj16AK6dsm+88UYWLVoEuMr89+3bx6xZswBITk5Gp9MxdOhQz/X69OlT4wTaUaNGVXucnOzqVt6zZw9HjhwhKCjI08bw8HDMZrPXRnoDBgzwBBuAgwcPMnDgQEwmk+fYiBEj6vU9UVW11ucqvwe4AmBgYCD+/v48++yzPPPMM1x66aX1eh8hRP1oSUTH8IpdwvGUhLtVLgE3/+Iasgof1RtdgKm2S4oOqt49N2FhYWRlZREVFUVoaGiN69y4N9R0OBxN2kjf8qvoQfHNezeV22+/3dPzAhATE+O5HxERQY8ePejRowdLly5lwIABDBs2jH79+gGuoanBgwdz7NgxFi9ezMSJE4mPj2+ytgGUlJQwdOjQGufrREZGeu43xeKRPXu6euKSk5MZMmRIteeTk5Pp1auX17G//e1vzJo1i8DAQKKjo71+/oODgz3VVJUVFBSg1WplwUshGsDA1dg12+iWmEXKgS6kpKR4SsI9Q1KJieQu2g1IlZSoWb3DzerVqz2ThdesWdNsDWptXHNemn9oqLnVd7J3bGws11xzDQ8++CBff/01gCfsvPXWW3z44Yf85z//8Zzfp08f7HY7O3bs8AxLHTx4kIKCgmrX3rJlCzfddJPXY3e4OP/88/nkk0+IiooiOLj+EwN79+7N//73PywWi2dO0LZt2+p8zZAhQ+jTpw8vv/wy1157rde8mz179rBy5UqvrxHOBMDa2vDxxx97tQFcvVyJiYno9fp6fz1CdHQGLqOMh+mWtI+UA11ITU1l7NixXiXgUaofhfml6IL9CBta8/+XomOr97DUuHHj0Ol0nvt13UTLKykpYffu3Z4KobS0NHbv3u01/FRfd999N99++y3bt2/3HJszZw7PPPMMqqp6VWT17t2biy++mD/96U/88ssv7Nixgzlz5uDnV73XaenSpSxatIhDhw7xyCOPsHXrVk8l1g033EBERASXX345GzZsIC0tjbVr1/KXv/zFM8Zek+uvvx6n08ltt91GcnIyP/30Ey+88AJAratoK4rC22+/zf79+7nqqqvYunUrGRkZLF26lOnTpzN16lT+9Kc/1fv7dcMNN6AoCjfddBM7duzgyJEjLFq0iFdeeYV777233tcRQoCGYAxcQmyS63eXuyS8cgm4bnc2AJHj+qPRaX3ZXNFKNXqdm/z8fF544QVuvfVWbr31Vl588UXy8vKasm2iAbZv386QIUM8PSHz589nyJAh/POf/2zwtfr168eUKVO8Xnvdddeh0+m47rrrvOa3gGvib0xMDOPGjePKK6/ktttu8+zuW9ljjz3Gxx9/zMCBA3nvvff46KOPPENf/v7+rF+/nri4OK688kr69u3LrbfeitlsrrMnJzg4mG+//Zbdu3czePBg/vGPf3jaXbWdlY0ZM4YtW7ag1Wq55JJLiI+P5+qrr+byyy/n22+/Raut/y/M0NBQNmzYgM1m4/e//z2DBw/m3//+Ny+99FKDQpIQwsXI1fgHlhERfRpwDUd5SsC7xZK/6SAAUbLdgqiFotY1q7IW69evZ/r06YSEhDBs2DAAduzYQUFBAd9++y1jx45t8oY2laKiIkJCQigsLKz2oWk2m0lLSyMxMbHOD8aOKD09ne7du7Nt2zbOP/98XzenTh988AGzZ8+msLCwxh6kmjidTm699VZ++ukn1q1b55mX01zkZ02I2qk4KGAEW9cnsGvTcPr27UtxcTHHjh1jVMJ5aJbswNQljGGL/lLnPoeifanr87uqes+5qezOO+/kmmuu4Y033vD8hetwOPjzn//MnXfeyd69extzWdEK2Ww2Tp8+zUMPPcTvfve7Vhls3nvvPZKSkujatSt79uzh/vvv5+qrr653sAHQaDS88847vPrqq2zYsKHZw40QonYKWozMIDbpc3ZtGk5qaqpnXSm//aex4JpILMFG1KZR4ebIkSN89tlnXl33Wq2W+fPn89577zVZ44Tvbdy4kQkTJtCrV69qKwq3FtnZ2fzzn/8kOzubLl26MHPmTJ566qkGX0ej0XD33Xc3QwuFEA1lZCZRMa9hNJmxmF3HwkJDsax3VSZGysJ9og6NCjfnn38+ycnJnuXv3ZKTkxk0aFCTNEy0DuPHj69zPZjW4L777uO+++7zdTOEEE1ISw/0miF0TcwgNdm1NENnTSA4swnq3RX/bhE+bqFozeodbn799VfP/b/85S/cfffdHDlyhN/97neAq6z3tdde45lnnmn6VgohhOhwjFxNbOIST7gJOFwISK+NOLt6h5vBgwejKIrXX/E1/bV8/fXXc8011zRN64QQQnRYBn5PQPB/XQ9UFf2+XNAoRI6THcBF3eodbmranVUIIYRoLhpCyMsaA4CpXEWjQtiw7hjCAn3cMtHa1TvcNPVy+0IIIcTZZKbEAcVEnnRt6yPbLYj6aNSEYrf9+/eTkZHhKdFz+/3vf39OjRJCCCHMZjMnjpcAEHXSiUMLYSN7neVVQjQy3KSmpvKHP/yBvXv3es3Dca850L42zhRCCOEL7l3AAxUVk1klJ1rDybxTdPPv5uumiVauUdsv3H333SQmJpKTk4O/vz+//fYb69evZ9iwYaxdu7aJmyjaIkVR+OqrrwDXLyhFUTz7XgkhRH2kpKQAEOLaSorcaC1HUvb5sEWirWhUuNm8eTOPP/44ERERaDQaNBoNF1xwAQsWLOAvf/lLU7dR1MOCBQsYPnw4QUFBREVFccUVV3Dw4MGzvk5RFM8tODiY4cOHe3YDb+82bdrEtGnTCAsLw2QyMWDAAF566aVqPY+Vv0chISGMGTOG1atXe54fP34899xzT7XrL1myhNDQ0Gb+KoRon1RV9RSyhGbbINBBQaiGlFRZAV+cXaPCjcPhICgoCICIiAhOnDgBuCYd1+cDVTS9devWceedd7JlyxZWrFiBzWZjypQplJaWnvW1ixcvJisri+3btzNmzBhmzJjRJrbQqDrXqyG+/PJLxo0bR7du3VizZg0HDhzg7rvv5sknn+Taa6+ttnCh+3u0ceNGIiIiuOyyyzwb+Qkhmp57F3CtqhBc4CR8fAhoFHJPOigpKfF180Qr16hw079/f/bs2QPAyJEjee6559i4cSOPP/44SUlJTdpAUT/Lli1j1qxZnHfeeQwaNIglS5aQkZHBjh07zvra0NBQOnfuTK9evXjiiSew2+2sWbMGVVXp0aMHL7zwgtf5u3fvRlEUjhw5AsDhw4cZO3YsJpOJfv36sWLFihrf58CBA4wePRqTyUT//v1Zt26d1/P79u3jkksuITAwkOjoaG688UZOnTrleX78+PHMmzePe+65h4iICKZOnQrAN998Q8+ePTGZTEyYMIF3330XRVEoKCiosR2lpaXMnTuX3//+97z55psMHjyYhIQE5syZw7vvvstnn33Gp59+WuP3qH///rzxxhuUl5fX+nUKIc6dZ0gq34FGhdiplxHZOQeAw6kbfNk00QY0Ktw89NBDOJ1OAB5//HHS0tK48MIL+eGHH/j3v//dpA30NVVVcZitPrmdy7YHhYWulTzDw8Pr/Rq73c4777wDgMFgQFEUbrnlFhYvXux13uLFixk7diw9evTA6XRy5ZVXYjAY+OWXX1i4cCH3339/jdf/29/+xr333suuXbsYNWoU06dP5/Tp0wAUFBQwceJEhgwZwvbt21m2bBknT57k6quv9rrGu+++i8FgYOPGjSxcuJC0tDRmzJjBFVdcwZ49e/jTn/7EP/7xjzq/zuXLl3P69Gn++te/Vntu+vTp9OrVi48++qjW17s35DyXniMhRN3cPaOhpxz4detEUI/exCe5amBSUnf6smmiDWhUtZT7L2aAHj16cODAAfLy8ggLC2t3u7Q6LTY2XfG0T9579Fd/R2syNPh1TqeTe+65hzFjxtC//9lX8rzuuuvQarWUl5fjdDpJSEjwhIpZs2bxz3/+k61btzJixAhsNhsffvihpzdn5cqVHDhwgJ9++omYmBgAnn76aS655JJq7zNv3jyuuuoqAN544w2WLVvGO++8w3333cd//vMfhgwZwtNPn/leL1q0iNjYWA4dOkSvXq7yz549e/Lcc895znnggQfo3bs3zz//PAC9e/dm3759dW6ceejQIQD69u1b4/N9+vTxnFNVWVkZDz30EFqtlnHjxtX6HkKIxjObzRw/fhyAsDwnUVe7dgDv0X0U2zcdJCPNgcNpQasx+rilorVqVM9NZZmZmWRmZhIeHt7ugk1bdeedd7Jv3z4+/vhjz7Hbb7+dwMBAz62yl19+md27d/Pjjz/Sr18/3n77bU+PT0xMDJdeeimLFi0C4Ntvv8VisTBz5kzAtVlqbGysJ9gAjBo1qsZ2VT6u0+kYNmwYycnJAOzZs4c1a9Z4tbFPnz7Ame5pgKFDh3pd8+DBgwwfPtzr2IgRI+rxXaLOnjGDwTtUXnfddQQGBhIUFMTnn3/OO++8w8CBspiYEM3BXQLuV+rEZFY9e0nFdpmGyc+C1WLg6PHvfdxK0Zo1qufGbrfz2GOP8e9//9szsSswMJC77rqLRx55BL1e36SN9CWNUc/or/7us/duqHnz5vHdd9+xfv16unU7sxbE448/XuMwDEDnzp3p0aMHPXr0YPHixUybNo39+/cTFRUFwJw5c7jxxht5+eWXWbx4Mddccw3+/v6N+6JqUVJSwvTp03n22WerPdelSxfP/YCAgHN+r549ewKuYDZ69OhqzycnJzN48GCvYy+//DKTJk0iJCSEyMhIr+eCg4M9w4CVFRQUEBIScs7tFaKjcf9BE5bnJLhfLH5dXH9saTVG4hJ1HNoPR1J/ISn2Sl82U7RijQo3d911F1988QXPPfec56/xzZs38+ijj3L69GneeOONJm2kLymK0qihoZamqip33XUXX375JWvXriUxMdHr+aioKE9YqcuIESMYOnQoTz31FP/6178AmDZtGgEBAZ6hpPXr13vO79u3L5mZmWRlZXlCyJYtW2q89pYtWxg7dizgCsg7duxg3rx5AJx//vl8/vnnJCQkoNPV/8eyd+/e/PDDD17Htm3bVudrpk6dSnh4OC+++GK1cPPNN99w+PBhXnnlFa/j7gBYWxuWL19e7fjOnTs9w2lCiPpRVdUz3yYsz0nUH717SLsnDePQ/l0cTXHgHJeHhvrPKxQdiNoIwcHB6g8//FDt+Pfff68GBwc35pItprCwUAXUwsLCas+Vl5er+/fvV8vLy33QsnNzxx13qCEhIeratWvVrKwsz62srKzO1wHql19+6XXshx9+UI1Go3rs2DHPsb///e+qwWBQ+/bt63Wuw+FQ+/Xrp06ePFndvXu3un79enXo0KFe101LS1MBNS4uTv3iiy/U5ORk9bbbblMDAwPV3NxcVVVV9fjx42pkZKQ6Y8YMdevWreqRI0fUZcuWqbNmzVLtdruqqqo6btw49e677/Z6/9TUVFWv16v33XefevDgQfWTTz5Ru3XrpgJqQUFBrV/30qVLVa1Wq86dO1fds2ePmpaWpr799ttqWFiYOnfu3LN+jypLSUlRTSaTetddd6l79uxRDxw4oL744ouqTqdTf/zxxxpf05Z/1oRoTllZWeqCBQvUZ598Wl136WOqtbDU6/mSkhJ1wYKn1QULFqi5RW/5qJXCF+r6/K6qUXNujEYjCQkJ1Y4nJiZWm6sgWsYbb7xBYWEh48ePp0uXLp7bJ5980uBrXXzxxSQmJnpNyr311luxWq3Mnj3b61yNRsOXX35JeXk5I0aMYM6cObVO5n3mmWd45plnGDRoED///DPffPMNERERgGtuz8aNG3E4HEyZMoUBAwZwzz33EBoaikZT+49pYmIin332GV988QUDBw7kjTfe8FRLGY21TzacMWMGa9asISMjgwsvvJDExETmzJnDAw88wJtvvlnv7xVAUlIS69ev58CBA0yaNImRI0fy6aefsnTpUi6++OIGXUuIjs7daxOS76TTsJ7og72HwAMCAoju4hqyP5K6scXbJ9oGRVUbXm/8+OOPc+DAARYvXuz5ALFYLNx666307NmTRx55pMkb2lSKiooICQmhsLCQ4OBgr+fMZjNpaWkkJiZiMpl81MLWacOGDVx00UVkZmYSHR3t6+bU6amnnmLhwoVkZmbW+zVms5nLL7+czMxM1q1bV21eTVOTnzUhava///2PY8eOkXTIxoRZVxI59rxq56zfsJxNG3eS2PswV/3hb+jo44OWipZW1+d3VfWe3HDlld4Tt1auXEm3bt0YNGgQ4Kp2sVqtXHTRRY1osmitLBYLubm5PProo8ycObNVBpvXX3+d4cOH06lTJzZu3Mjzzz/vmctTXyaTia+//ppXXnmF9evXe0rWhRAtp3IJeESZjvBadgDv0b0/mzbu5Hh6HOWOpQRpH27JZoo2oN7hpmrVR9Vf/rGxsU3TItGqfPTRR9x6660MHjyY9957z9fNqdHhw4d58sknycvLIy4ujnvvvZcHH3ywwdcxmUw88MADzdBCIUR9pKWleUrAu404D20tFaOdO3fG5KfFXG4k4/hq+sXZURpXHyPaqXr/NFRdpVZ0DLNmzWLWrFm+bkadXn75ZV5++WVfN0MIcY5SKrZ0CctzEjWj9nWkNBoNSYk92b//AJmpwfSMW4cBGTUQZ5zTIn65ubn8/PPP/Pzzz+Tm5jZVm4QQQnQwqqqScsgVbiLtRkIGxNd5fvfurvWqMlPjsfJpneeKjqdR4aa0tJRbbrmFLl26MHbsWMaOHUtMTAy33norZWVlTd1GIYQQ7dzJkycpt1nQOFR6DhuAUkeVJOBZy+t0TiT5xT/jpKAFWinaikaFm/nz57Nu3Tq+/fZbCgoKKCgo4Ouvv2bdunXce++9Td1GIYQQ7dyR5IMAhOY76XLR4LOe7+/v71k4NDM1BitfN2fzRBvTqHDj3lvnkksuITg4mODgYKZNm8Zbb73FZ5991tRtFEII0c4d2uvaZy5KCSAgqXO9XpOUlAS4hqYsLG22tom2p1HhpqysrMaS4KioKBmWEkII0SBms5mc0gIAeg/pX+/Xde/eHYBj6bHYHHtwcLg5mifaoEaFm1GjRvHII49gNps9x8rLy3nsscdq3RFaCCGEqMmh3ftAAb9SJ0mTh9f7dV26dMHPzw+bxUj28c7SeyM8GhVuXnnlFTZu3Ei3bt246KKLuOiii4iNjWXTpk2ezRZFxzB+/HjuueceXzdDCNGGHdj+KwCdNYEYI0POcvYZiqJUGppKwMLnqDiapY2ibWlUuBkwYACHDx9mwYIFDB48mMGDB/PMM89w+PBhzjuv+lLZovnNmjULRVGq3Vrb3kZr165FURQKCgp83RQhRCvgdDo5VuBaSqRn/74Nfr0n3KQkopKNjQ1N2j7RNjV4SUebzUafPn347rvvmDt3bnO0STTSxRdfXG2xxbo2j2zLVFXF4XCg08mqpEK0Zek792PVqWgcKv2m/K7Br3eXhOflhlNSFIAheCkGxjdxK0Vb0+CeG71e7zXXpr1TVRWr1eqTW0P3NDUajXTu3NnrFhYWBri6b//73/9y2WWX4e/vT9++fdm8eTNHjhxh/PjxBAQEMHr0aFJSUjzXmzVrFldccYXXe9xzzz2MHz++1ja8//77DBs2jKCgIDp37sz1119PTk4OAOnp6UyYMAGAsLAwFEXxrH5ssVj4y1/+QlRUFCaTiQsuuIBt27Z5ruvu8fnxxx8ZOnQoRqORn3/+uUHfHyFE67N/4w7AVSVlCglq8Ov9/f2JiYkB4FhaPFaW4aSoSdso2p5G/dl755138uyzz/L22283yV/Or732Gs8//zzZ2dkMGjSIV199lREjRpz1dR9//DHXXXcdl19+OV999dU5t6MmNpuNl156qVmufTbz58/HYDA02fWeeOIJXnrpJV566SXuv/9+rr/+epKSknjwwQeJi4vjlltuYd68efz444+Nfg+bzcYTTzxB7969ycnJYf78+cyaNYsffviB2NhYPv/8c6666ioOHjxIcHAwfn5+ANx33318/vnnvPvuu8THx/Pcc88xdepUjhw5Qnh4uOf6DzzwAC+88AJJSUme4CaEaJtUh5PMU9kQAN179Wj0dZKSkjhx4gSZKf3pM2g/Vr7FxA1N2FLR1jQqmWzbto1Vq1axfPlyBgwYQEBAgNfzX3zxRb2v9cknnzB//nwWLlzIyJEjeeWVV5g6dSoHDx4kKiqq1telp6fz17/+lQsvvLAxX0K79N133xEYGOh17O9//zt///vfAZg9ezZXX301APfffz+jRo3i4YcfZurUqQDcfffdzJ49+5zacMstt3juJyUl8e9//5vhw4dTUlJCYGCgJ6hERUURGhoKuFa8fuONN1iyZAmXXHIJAG+99RYrVqzgnXfe4W9/+5vnmo8//jiTJ08+pzYKIVqHkzsOUejvBBT6jx/Z6Ot0796dn3/+mePp0TgcGqzaTyXcdHCNCjehoaHVdgVvrJdeeom5c+d6PlQXLlzI999/z6JFi2rdodnhcHDDDTfw2GOPsWHDhmadnKrX65k/f36zXf9s790QEyZM4I033vA6VrnXY+DAMxvRudcpGjBggNcxs9lMUVERwcHBjWkyO3bs4NFHH2XPnj3k5+fjdDoByMjIoF+/fjW+JiUlBZvNxpgxYzzH9Ho9I0aMIDk52evcYcOGNapdQojWZ/+6raAoBClGwiI6Nfo6nTt3xt/fn7KyMk4eiyEmfjsOUtDSvQlbK9qSBoUbp9PJ888/z6FDh7BarUycOJFHH33UM7TQUFarlR07dvDggw96jmk0GiZNmsTmzZtrfd3jjz9OVFQUt956Kxs21D0z3mKxYLFYPI+Liho2FqsoSpMODTWngIAAevSovWu3clhSFKXWY+5AotFoqs37sdlstV6/tLSUqVOnMnXqVD744AMiIyPJyMhg6tSpWK3Whn9BNajaSyiEaJscFhtHs49DpEJSxaTgxlIUhcTERH777TeOp15ITPxHWPgMf+5votaKtqZBE4qfeuop/v73vxMYGEjXrl3597//zZ133tnoNz916hQOh6PaasfR0dFkZ2fX+Jqff/6Zd955h7feeqte77FgwQJCQkI8t9jY2Ea3t6OJjIwkKyvL69ju3btrPf/AgQOcPn2aZ555hgsvvJA+ffp4JhO7uYOiw3FmLYru3btjMBjYuHGj55jNZmPbtm219vYIIdq201sOkFexpE2f4YPO+Xru1YozUrsCYOUzVJznfF3RNjUo3Lz33nu8/vrr/PTTT3z11Vd8++23fPDBB56/9JtbcXExN954I2+99RYRERH1es2DDz5IYWGh55aZmdnMrfQdi8VCdna21+3UqVONvt7EiRPZvn077733HocPH+aRRx5h3759tZ4fFxeHwWDg1VdfJTU1lW+++YYnnnjC65z4+HgUReG7774jNzeXkpISAgICuOOOO/jb3/7GsmXL2L9/P3PnzqWsrIxbb7210e0XQrReh9fswGZQ0CmaJvmjMzExEUVROJ1robSoC05OYGfj2V8o2qUGhZuMjAymTZvmeTxp0iQUReHEiRONevOIiAi0Wi0nT570On7y5Ek6d66+cVpKSgrp6elMnz4dnU6HTqfjvffe45tvvkGn03mVMbsZjUbP5p7uW3u1bNkyunTp4nW74IILGn29qVOn8vDDD3PfffcxfPhwiouLuemmm2o9PzIykiVLlrB06VL69evHM888wwsvvOB1TteuXXnsscd44IEHiI6OZt68eQA888wzXHXVVdx4442cf/75HDlyhJ9++kkqooRoh2yFpWRkHwMgNqZbk1Td+vn5eXYJP5Hq+pyy8Ok5X1e0TYragMVUtFot2dnZREZGeo4FBQXx66+/ehZSaqiRI0cyYsQIXn31VcA13yMuLo558+ZVm1BsNps5cuSI17GHHnqI4uJi/vWvf9GrV6+zzo8pKioiJCSEwsLCakHHbDaTlpZGYmIiJpOpUV+PEPUhP2uiIzvx7Va+3LSS4hANU6dOZciQIU1y3Y0bN7JhwwZ69opiwpUPAybC2I1Cw9fPEa1PXZ/fVTUoLquqyqxZs7xWvTWbzdx+++1eEz0bUgo+f/58br75ZoYNG8aIESN45ZVXKC0t9VRP3XTTTXTt2pUFCxZgMpno3997x1h3OXHV40IIIVqnY2t2UxzpKmBwb5/QFJKSktiwYQNH0wtQHT1QtEew8j1Grm2y9xBtQ4PCzc0331zt2B//+MdzasA111xDbm4u//znP8nOzmbw4MEsW7bMM8k4IyMDjaZRW2AJIYRoZcqz8jiWmw1RBjqFhhESUv+NMs+mckn46WN/ICL+eSx8KuGmA2pQuKm6b1FTmTdvnmfuRVVr166t87VLlixp+gYJIYRoFrmr91LQSQtA9149m/Ta7l3C9+3bx7HU7kTEK9j5BQfpaElo0vcSrZt0iQghhGgRqqpycvUe8sNdHz1NOSTl5r5maspJdIwFwMJnTf4+onWTcFODhm5YKURDyc+Y6IhKDp3gVGE+NoOCXq9vlnXH3CXhp06dwlr0B0DWvOmIJNxU4l6tt6yszMctEe2de8VmrVbr45YI0XJyVv9KfifXx05CQkKz/Pz7+fl5dgnPTI1HIQgnmdjZ0uTvJVqvc19coB3RarWEhoZ6VtX19/f3bEkgRFNxOp3k5ubi7+/fJOt7CNEWqA4Huev2kZ/oCjTNMSTllpSUxPHjx0lNyaTX4N9j4QMsfIqe0c32nqJ1kd+sVbgXD6y6bYAQTUmj0RAXFyfhWXQY+btSKSsppTjYta5Tc4ab7t27u0rCjx5F67gKtB9g5XtUnkJB9qfrCCTcVKEoCl26dCEqKqrOTSKFOBcGg0GWOBAdSs6qXykI04DiWp2+KUvAq4qOjvaUhGdnRhOakICT9Io1b65utvcVrYeEm1potVqZDyGEEE3AUW7h9KYDLTIkBd4l4ampqYxKmEk5z2NhqYSbDkL+dBRCCNGsTm86gMNioyCiZcINnNklPDU1FQMzAQU7m3DQfjdPFmdIuBFCCNGsclb/Smmggk3nGpJtjhLwqhISEjwl4SWFgegYA7jKwkX7J+FGCCFEs7Hml5C/K9VTAh4fH98iQ/5+fn507doVcPXeGJkJgIWlqMg6U+2dhBshhBDNJnfdPnCqFHX1A84MF7UE9/BXSkoKBqYBATg5ip2tLdYG4RsSboQQQjSbnFW/YtNBocEOtMx8Gzf3ex09ehSH3YCBywCwsrTF2iB8Q8KNEEKIZlGWmUvJ4RMUdNKi4ioBDw4ObrH3j46OJiAgAJvNxrFjxzyVUha+RaW8xdohWp6EGyGEEM0iZ/VeAMp6uta0acleGzhTEg6uoSkdI9AQD5Rg5ccWbYtoWRJuhBBCNDlVVcld8ysqcMrPNSTVkvNt3Dy7hKemoqDByAwALHza4m0RLUfCjRBCiCZXnJyJObuA8ggDZpsVg8FAt27dWrwd7l3CT58+TUFBAYaKcGPnZxwcb/H2iJYh4UYIIUSTy1n9KwDmgZFAy5WAV2UymbxKwrXEoWMUoGLl8xZvj2gZEm6EEEI0KafNTu663wDIC3Id88WQlFvloSmg0po3n8qaN+2UhBshhBBNKn/7EezF5RARQE5RHtDyk4krq1wSbrfbMXAp4IeTNOzs8Fm7RPORcCOEEKJJ5axxVUk5RsaiqiqRkZEtWgJeVXR0NIGBgZ6ScIXAioADVplY3C5JuBFCCNFk7KVm8rYcBPBsueDLXhtwlYQnJiYCrpJwwLPmjZVvZM2bdkjCjRBCiCZzamMyTqsdU1wnMk9lA74PN+C9SziAjlFo6IZKMVaW+7JpohlIuBFCCNFk3FVSujHdKSsr81kJeFXuXcLdJeEKGk9ZuAxNtT8SboQQQjQJy6kiCvekAVDU2QC4QoUvSsCrqloSDngW9LOxHidZPmubaHoSboQQQjSJ3LV7QYXg/nFk5LjCQmsYknJzD025591oSUTHCMCJhS982DLR1CTcCCGEaBLuIangC/tw/Lhr9d/WFG7cbcnIyMBud20JcWYzzaWy5k07IuFGCCHEOStNO0lp6kkUnYbibn4APi8BryoqKspTEp6ZmQmAgcsAE04O42C3T9snmo6EGyGEEOcsZ42r1yZ8RC+OnjgGtK5eG6i+SziAQhAGpgGymWZ7IuFGCCHEOVGdTnIrFu6LnDCAtDTXpOLWFm6g+lYMcGY7BteaN2aftEs0LQk3QgghzknhvgwsuUVoA4xY44NbVQl4Ve6S8Ly8PAoKCgDQMQYNXVApwMYK3zZQNAkJN0IIIc5JbsVE4ogL+pGWcRRoPSXgVZlMJk/oOjM0pcXAVYBrYrFo+yTcCCGEaDSn1UbuBtcO4FETB3qGe1rjkJRbXUNTNtbiJMcn7RJNR8KNEEKIRsvbehhHqQVDRDCGHlGcOHECaN3hxr3ejXuXcAAtPdAxFHDImjftgIQbIYQQjeZe2yZq4gDSj7qGpFpbCXhVkZGRBAUFYbfbycjI8Bw3eCYWy5o3bZ2EGyGEEI1iKy4jb+shwDUk5Z7D0pp7bcB7l/DKQ1MGfg8YcXAAB3t91DrRFCTcCCGEaJRTG/aj2p0EJEXjHx/VqkvAq6pp3o2GEAxMBWRicVsn4UYIIUSjeIakJgwkOzu7VZeAV5WQkIBGoyEvL4/8/HzPcUPFdgxWvkTF6qvmiXMk4UYIIUSDmbPzKdqXAQpEju/vGZJqrSXgVdW0SziAnrEoRKOSj41VvmqeOEcSboQQQjRYzlrXnJSQQYkYI0M8AcFdidQWuNtaOdwoaDF61ryR7RjaKgk3QgghGkRV1UpVUgMpKyvzlIC7J+q2Be55N0ePHsVms3mOn1nzZjVOTvmkbeLcSLgRQgjRIKUp2ZRnnEJj0BExpq9nInFrLwGvqnJJuHuXcAAtvdAyGLBj5UuftU80noQbIYQQDeLutQkf2QtdgKlNrEpck5p2CXdz997I0FTbJOFGCCFEvakOJzkVO4BHTRyIqqqenpu2NN/GraaScAADlwMGHOzHzj4ftEycCwk3Qggh6q1gTxq2/BJ0QX6EDevhKQE3Go2e6qO2xF0Snp+f71USriEMPZMBWfOmLZJwI4QQot7cQ1KR485Do9d5hnPi4+PbRAl4VUajsdou4Z7nvNa8sVV7rWi9JNwIIYSoF4fZyumNyYBrSApokyXgVdU2NKVnPAqRqJzGxhpfNE00koQbIYQQ9XJ6y0Ec5VZMnUMJ6hvbZkvAq3IHs4yMDK+ScAUdRq4EZGJxWyPhRgghRL3kuoekJgxEUZQ2WwJeVURERI27hMOZncJtrMRJni+aJxpBwo0QQoizshaUkrf9CABREwcA7WNICrxLwqsOTenoi5YBgI1yXkCl3ActFA0l4UYIIcRZnVq/D5wqgT1j8I+NRFXVNru+TU3cAa3qpGIAEzcDYOFdCrkQC5+g4mjR9omGkXAjhBDirHJWV6xtc5FrInFWVhbl5eVttgS8qvj4eDQaDQUFBeTleQ8/GbiWAP6Nhq44yaKU+RQxFStrfdNYcVYSboQQQtSp/MRpig8cA41C5NjzgDPDN221BLyqyiXhVYemFBSMXEUI6/HjIRSCcZBMCTdQxHWyyF8rJOFGCCFEndy9NmFDkjCEBwHtZ75NZTXtEl6Zggk/7iCEjRiZC+ixs54iLqaEu3FwvAVbK+oi4UYIIUStqu4ADniVgLeH+TZute0SXpWGcAJ4lBDWV2zToGLlMwq5kDKewklhC7VY1EbCjRBCiFoVHzyO+UQeGqOeTqP7AHiVgAcFBfmyeU0qIiKC4OBgHA5HtZLwmmiJI5DXCeZ7dIwCLJh5nUJGY+YtVKzN32hRIwk3QgghauVe26bT6D5o/YxA+xySgrp3Ca+LjsEEsZRAlqChJyoFlPEohYzHwteoqM3VZFELCTdCCCFq5LQ7yF3nmizrHpJqbyXgVVVe70ZV6x9KFBQMTCaElfjzHApRODlKKX+miMuwsaW5mixq0CrCzWuvvUZCQgImk4mRI0eydevWWs996623uPDCCwkLCyMsLIxJkybVeb4QQojGKdiZgq2wDH2IP2Hnuz7021sJeFWVS8Ir7xJeXwo6TNxAKBvx46+APw52U8xVFDMLB4ebvtGiGp+Hm08++YT58+fzyCOPsHPnTgYNGsTUqVPJycmp8fy1a9dy3XXXsWbNGjZv3kxsbCxTpkzh+HGZpS6EEE3JswP4+AEoFeXe7uGahISEdlECXpXRaCQ2NhZo2NBUVQr++PF/hLIJIzcBWmysoJCJlHIfTmr+jBNNw+fh5qWXXmLu3LnMnj2bfv36sXDhQvz9/Vm0aFGN53/wwQf8+c9/ZvDgwfTp04e3334bp9PJqlWrajzfYrFQVFTkdRNCCFE3e5mF05sPAGeGpIB2PSTlVttWDI2hIZIAFhDCavRcDDix8AEFjKGMF1EpPef3ENX5NNxYrVZ27NjBpEmTPMc0Gg2TJk1i8+bN9bpGWVkZNpuN8PDwGp9fsGABISEhnps7kQshhKjd6U3JOC12/Lp2IrBXDOD6fZuVlQW073BT2y7h50JLD4J4hyC+QMsQoAwzL1HAGMy8j4q9Sd5HuPg03Jw6dQqHw0F0dLTX8ejoaLKzs+t1jfvvv5+YmBivgFTZgw8+SGFhoeeWmZl5zu0WQoj2zrPdwkTXDuBwpgQ8KiqqXZWAV9WpUydPSfjRo0eb9Np6RhLMtwSyEA0JqORSxgMUchFWlktlVRPx+bDUuXjmmWf4+OOP+fLLLzGZTDWeYzQaCQ4O9roJIYSonfV0MQW7XUMykRMGeI53hCEpcJWEn2214nO6PgoGphPCGvx5AoUwnByhhNkUMwM7u5r8PTsan4abiIgItFotJ0+e9Dp+8uRJOnfuXOdrX3jhBZ555hmWL1/OwIED6zxXCCFE/eWuc+0AHtS3G34xriF/p9PZYcIN4LXeTUNKwhtCwYCJWwhhEybmASbsbKGIyyjhdhykN8v7dgQ+DTcGg4GhQ4d6TQZ2Tw4eNWpUra977rnneOKJJ1i2bBnDhg1riaYKIUSHUXW7BYDs7Ox2XQJelXtD0MLCwmq7hDc1DcH482DFdg5XAwpWvqWQ8ZTyT5w07/u3Rz4flpo/fz5vvfUW7777LsnJydxxxx2UlpYye/ZsAG666SYefPBBz/nPPvssDz/8MIsWLSIhIYHs7Gyys7MpKSnx1ZcghBDtRllGLiVHslC0Gs8O4ND+S8CrMhgMte4S3ly0dCWQlwnmJ/SMB2xYeIdCxlDOa6iUt0g72gOfh5trrrmGF154gX/+858MHjyY3bt3s2zZMs8k44yMDM/sfIA33ngDq9XKjBkz6NKli+f2wgsv+OpLEEKIdsPdaxM2rAf6kADP8Y40JOXmnndzLuvdNIaO8wjiA4L4CC3noVJEOU9TyFgsLEXF0aLtaYt0vm4AwLx585g3b16Nz61du9brcXp6evM3SAghOiDV6SRnzZkqKbeOUgJeVVJSEqtXryYzMxOr1YrBYGjR99czlmCWYeULynkWJyco5R7MvIk/D6NnbIu2py3xec+NEEKI1qFofyaWkwVo/Q2Ej+zlOe7utWnvJeBVVS4Jr88u4c1BQYORGYSwHj/+gUIwDvZTzHUUcT12fvNJu1o7CTdCCCGAM0NSEWP6oTWd6aXoiENS4F0S3tJDU9Xagh9+/JkQNmJkDqDHzjqKmEoJ9+BAtiCqTMKNEEIInDY7p9a7egEiKw1JOZ1Oz+J9HS3cQON3CW8uGsIJ4DFCWIeBywEVK0spZCxlPI0T2WIIJNwIIYQA8rcfwV5ixtApiNCBCZ7jHa0EvKqWLAlvCC3xBPI6wXyHjt8BZsy8RiGjMfM2KlZfN9GnJNwIIYQgZ5V7B/D+KNozHw0drQS8KoPB4NmTcNeuXTidTh+3yJuOIQTxGYEsRkNPVPIp4xEKGY+Fbzrsdg4SboQQooOzl5Rz+peDgHeVFHTc+TaV9enTB4Dt27fz3nvvcfx465rf4trOYQohrMSfZ1GIwslRSrmDIqZj4xdfN7HFSbgRQogO7tTPyag2B/7xkQQkndn6pqOWgFc1aNAgLrroIoxGI9nZ2bz//vt8//33lJaW+rppXhR0mPgjofyMH/cC/jjYRTFXUsxsHBzxdRNbjIQbIYTo4Cpvt+DeARw6bgl4VYqiMHz4cG677TYGDHBtJLp3717efPNNtm3b1uqGqhQC8GM+oWzEyB8BLTaWU8hESnkAJzm+bmKzk3AjhBAdmCW3kMK96YD3DuBwJty4y6E7uoCAAC699FJuvPFGOnfujMViYdWqVSxevJijR4/6unnVaIgigGcJYRV6pgIOLLxPAWMo5yVUWlfPU1OScCOEEB1Yztq9oELwgHhMUaGe4x1tF/CG6Nq1KzfddBMXX3wxfn5+5Obm8tFHH/H1119TVNT6SrG19CSIRQTxOVqGAGWU8yIFXICZ/6Fi93UTm5yEGyGE6MDcVVJVJxJnZWVhNps7bAn42Wg0GgYPHsxtt93G+eefj6IoJCcn89Zbb7F582bs9tYXGPT8jmC+JYA30BCPSg5l3E8hk7CyvF1VVkm4EUKIDqo0NZuy9BwUvZaIC/p5PefutUlISECjkY+K2vj5+TFlyhRmzZpFt27dsNlsrFu3jnfeecfnqxrXREHByO8JYS3+PI5CGE4OU8JsipmJnd2+bmKTkJ9YIYTooNwTicNH9EIf5Of1nMy3aZjo6GhuuOEGLrvsMgICAsjPz2fp0qV89tln5Ofn+7p51SgYMHErIWzExDzAhJ3NFHEpJdyBg9Y3h6ghJNwIIUQHpDqdrvk2VB+SKi0tlRLwRlAUhf79+3PbbbcxYsQINBoNR44c4e2332b9+vXYbDZfN7EaDSH48yAhrMfATEDByjcUMo5SHsFJ61mVuSEk3AghRAdU+Gs61lPF6AJNhA/v6fWcey+pqKgoAgMDfdG8Ns1oNDJx4kRuueUWEhIScDgcbNq0ibfeeosDBw60ij2qqtLSlUBeIZif0DEOsGHhbQoZQzmvo1Lu6yY2iIQbIYToQJxWG8c+30Tyk58CEHFhPzQGndc5MiTVNCIiIrjmmmu44oorCA4OpqioiK+++opPPvmEU6dO+bp5NdJxHsF8SBAfoqUfKkWU8xSFjMXCZ6i0rjV9aiPhRgghOgDV6SRn9a9sn/Mf0t5ajr3EjH9CFHHXj/M6T0rAm5aiKPTp04e5c+cyevRotFot6enpLFq0iNWrV2OxWHzdxBrpGUcwywjgFTTE4OQEpdxNERdjY72vm3dWurOfIoQQoi3L35VK2tvLKU3JBsAQEUTCTROJumiQ1yaZICXgzUWv1zN27FgGDhzIypUrOXLkCFu3buW3335jwoQJnHfeeV6rQ7cGClqMzMTAZZhZhJlXcfAbxVyHnvH48Q909Dv7hXxAUVvj4F8zKioqIiQkhMLCQoKDg33dHCGEaDalqdmkLVpJ/nbXnkJafyOx11xAzOUj0ZoMNb5mw4YNbNy4kT59+nDFFVe0YGs7lpSUFFauXOmppOrWrRuTJ08mOjraxy2rnZM8ynkFC+8BNlw1VzPx529oiGn292/I57eEGyGEaGcsuYUcfW8NJ1fuBhUUnYYulw0n7rqx6EMC6nztkiVLyM7OZtq0aQwcOLDOc8W5sdvtbNu2jU2bNmGz2VAUhSFDhnDhhRfi5+d39gv4iIN0ynkGK99WHDFhYg4m7kRD832uSripg4QbIUR7ZS81k/nJz5z4agtOq2uF3Iix55Ew6yL8YsLP+vrS0lJeffVVAObNmyeVUi2kqKiINWvWkJycDLgWBhw3bhwDBw5s1Qso2tlJGU9i5xcAFMLx4/8w8kcUau4ZPBcSbuog4UYI0d44bXayvt9OxofrsBe5SnaDB8STeOtkgvt0q/d19u3bx3fffUdUVBS33HJLczVX1OLo0aOsWLHCU0nVuXNnJk+e3KrnPqmo2FhBGU/hxDX8qSEBfx5Az2UoNN08Igk3dZBwI4RoL1RV5dT630hfsgpzlmvuhl9cBIm3TCZ8ZK8GT1D95ptv2L9/P6NGjWLcuHFnf4Focg6Hg507d/Lzzz97KqkGDBjA+PHjCQioe0jRl1TsWPiIcl5EJRcN8YSwtkl7cBry+S3VUkII0QYV/JpO2tvLKTl0AgB9WCDxN02g85TBKFptg68nJeCtg1arZfjw4fTr14+1a9eyd+9e9u7dy6FDh7jgggs4//zz0Tbiv29zU9Bh4kaMXImZ/6Klb7MMTdWXhBshhGhDStNzSF+8krxfDgGg9TPQbcYYul41qtYKqPqQEvDWJSAggEsvvZTBgwezYsUKsrOzWbVqFXv27GHy5MnEx8f7uok1UgjAj/m+boaEGyGEaAssp4vIeH8t2ct3gVMFjUKXacOIu2EchrBzn/jr3sE6MTGxVU9i7Wi6du3KTTfdxK+//sq6des4deoUH330EX379mXChAkyvaIWEm6EEKIVs5eaOfbZJo5/sQmnxVUB1WlMXxJmXYR/bESTvY8MSbVeGo2GwYMH07t3bzZs2MCuXbtITk7myJEjjB49muHDh6PTycd5ZfLdEEKIVshpd5D9ww4yPliLrbAMgOB+sSTOmUxwv7gmfa/S0lKys12rF0u4ab38/PyYMmUKgwYNYsWKFRw7dox169bx66+/MmnSJNkLrBIJN0II0YqoqsrpjcmkL15J+fE8APy6diLhlkl0Gt2nWZbod+8CHh0dLWvbtAHR0dHccMMN/Pbbb6xZs4b8/HyWLl1Kjx49uOiiiwgLC/N1E31Owo0QQrQShb9lkPb2coqTjwGgDw0g7o/j6Xzx+Wh0zVch455vI702bYeiKPTv35+ePXuyceNGtm/fzpEjR0hLS2PkyJGMGjUKvV7v62b6jIQbIYTwsbLMU6QvXsnpTQcA0Bj1dJsxmq5XjUbnb2zW93Y6nZ6eGwk3bY/RaGTixImeDTnT09PZtGkT+/btY+LEifTu3bvVbcjZEiTcCCGEj1jzS8j4YC1ZP+zwVEB1nno+8X8cj6FTUIu0QUrA24eIiAiuueYaDh48yOrVqykqKuKrr74iISGBSZMmERHRdJPP2wIJN0II0cIc5RaOfbGZY0s34jTbAAj/XW8SZl9EQHxUi7ZFSsDbD0VR6NOnD927d2fLli1s2bKF9PR0Fi1axLBhwxgzZgxGY/P2BLYWEm6EEKKFqA4H2T/t4uj7a7HllwAQ1LsriXMmEzIgwSdtkhLw9kev13PhhRcyYMAAVq5cyZEjR9i6dSu//fYbEyZM4Lzzzmv3Q1USbkSLs6t2cu0FWJxWdIoWnaJz/Yu24rHrpkXb7v8HFB2DqqrkbTlI2qKVlGe6NkU0dQkjYfYkIi7s57OfcykBb99CQ0OZMWMGKSkprFy5kvz8fL777jt2797N5MmTiY6O9nUTm42EG9HkVFWl0FlCji2fk/Y8TtryyLHnk1NxP89RhEr99mvVVg48aM4EIc+xyoFI531+DYHpzGNdjedoa3xNbe+pQ6toPI81inTpi+qKDhwj7e3lFO3LAEAX4k/c9ePoMm0oGr1vfwW7e22kBLx96969O/Hx8Wzbto1NmzZx7NgxlixZwpAhQ7jwwgvx8/PzdRObnIQb0SgWp5Ucuyu85FSEl8r3LaqtztcbFD3+GhMO1YFddWDHgU21VzvPgQOH6sDSBvau16CpI1TpKsKT5iyBqVL44kzgMil6grUBBGkCCNaeuekV+V+4tSo/cZr0xas4tWE/ABqDjq5XjqLbzDHoAkw+bp2LO9zI4m/tn06nY9SoUZx33nmsWbOG5ORkdu7cSXJyMuPGjWPgwIHtas6V/GYUNXKqTvIcReTY8jjpCS4VvS/2PAodJXW+XkEhXBtMtD6cKF040bowovThROvCidKHEaIJrNYVr6oqTpyusFMReOyqwysAeZ5T7VUe13AOlc6t8RoO7Ngr3sPp9bjm8+2eYw7ViROn9/cMJ1bVifUswa4p+SlGV+jR+hNcOfhU3A+qdD9YG4BJMchQXzOzFpSS+eE6sr7fjupwggLRk4cQf+N4jJEhvm6eh5SAd0zBwcFcfvnlDB48mJUrV5Kbm8uyZcs8Q1XtpWJOwk0HVuoor6P3pQAHjjpf768xucKKLqwixIS5gow+nAhdaIN7FRRFQYurp6ItzOd3egJRTQHLOyQ5agxd3oGp1vNVB+WqhSJHKUWOUoqdZRQ7SnHgpFy1UG63cNKeV6826xUdQRp/rwDkvnmOe54LJEBjkuG2enKYrRz/agvHPv0ZR5kVgLDhPUm8ZRIBia1vbsOJEyc8JeAxMTG+bo5oYfHx8cyePZudO3eyYcMGsrOzef/99xkwYADjx48nICDA1008JxJu2jG76uCUvcAz1+VkxbwX91yYUmd5na/XoiFCF1rR23Km18UVaMIJ1La/cdqG0CgaDIoGAy2/CqiqqpQ6zRQ5KwKPo9Rzv3II8jx2lmJVbdhUO3mOIvIcRfV6HwWlhtBTQ09RxXNBWn90HWyoTHU4OblyN0ffW4P1dDEAgT26kDhnMqGDW1ePiMViISMjg6NHj3LkyBFASsA7Mo1Gw7Bhw+jbty9r165l79697N27l0OHDnHBBRdw/vnno9U238rYzUlRVbUNzGZoOkVFRYSEhFBYWNjmt4pXVZViZyknK8JK5eCSY8/jlL3wrBN3QzQBRFX0urhDjPt+uC4YrdI2f7BFdWantSL0lHqFHlc4KvMOR85SypzmRr2Pv8bkFXxqCkeV5w+ZNIYm/kpbhqqq5G87TNqilZSl5wBgjA4lYdZFRI47D6UVBAa73c6xY8c4evQoR48eJSsri8q/8hVFYcaMGTLnRgBw/PhxVqxY4amgi4iIYPLkycTHx/u4ZS4N+fyWcNPKWZ02r0qjqr0vFtVa5+v1is4zdOTpfak0jGTStIUBIOELdtVePfTU0CN0pueorN5VcJUZFH09eoTO3A/QmHw+b6j40HHS3llB4Z50AHSBJlcF1GXD0Rh813PldDrJysryhJljx47hcHgPL4eFhREfH++5+fv7+6i1ojVyOp38+uuvrFu3jvJyV+9+3759mTBhgs8/MyXc1KG1hRun6qTAUVxRMl0RXOz5FeXTeeQ7iut8vXvi7pnwElbR++K6H6oN8vkHgegYnKrTNVTmKKHIWdEbVNdwmbO0xgq5s9Gi8UyUDtJWnz8UqPHDqDFgUgwYFQNGjd71r6LHqHH929h5RObsfNKXrCJ37T4AFL2Wrlf8jm5XX4A+qOWHaVVVJTc31xNmMjMzsVgsXucEBgZ6hZmQkNYzqVm0XuXl5WzYsIFdu3ahqip6vZ7Ro0czfPhwdDrfBHgJN3XwRbgpc5q9Ko0q38+1F5z1F7yfYqw2YdfdAxOhC8Wg6bg7v4q2S1VVzGoNQ2VegajM67ly1XL2C9eDXtFhUPSuAFRD+DEoBkyain8VPcZSheCvMtH/dAzF7qqA0o9LJPiGYfhHhXle5wpTBnTNOJxbUFBAenq6J9CUlZV5PW80Gr3CTKdOneQPHNFoJ0+eZMWKFRw75tqpPiwsjEmTJvlkKFPCTR2aK9yUOc2kWI5V6n050xNT7Cyr87UaNETqQqsNHbnvB2r85JeTEIBNtbuGwKr2CFUaKit1lmFRbVicVs+/ZtWGVbU1eNhMa4VBazUMW6bFWO76fzCjr5ONf3BwKrb2a2nRVAk83gGqrmDldb5Gj7PUTv6xU+RkniQr8wTFhd6TwXU6HbGxscTHx5OQkEBUVJRMEBZNSlVVfvvtN9asWUNpaSkAPXr04KKLLiIsLKzF2iHhpg7NFW72lh/hiexFtT4fpPGvtfelky5EJu4K0cxUVcWm2l2BR7WeCT+qFYvThlm1YnX/67DC+mMEfJqG7rRrXltZnJG0q8PIPU/vOke1YXae+dei2qqtfdQYWisE5ugIytESdFKHX5H37wZVUSnt5KA8GqydFdRIPUadoSIc1RGUKh+vONekGAjXBROuDZYeYHFWFouFjRs3sn37dpxOJ1qtlpEjRzJq1Cj0+ub/+ZFwU4fmCjc5tnwWnFziFV6iPD0wYfhrWseKpEKIuuXvTCHt7eWUpp4EwBgZTPzNE4maMBBFW3ePiF21Y3basKoVvUWeXiPrmePOikDl7lWyWSjNKsR8vBh7VhnqaRtKpd/KKmAJUymOslEYbaMkwoGzGT5HgjT+dNKFEK4NIUIXQrguhE7aYNexivvGNlrZJprWqVOnWLlyJenp6YBrYcCJEyfSu3fvZh1lkHBTh9Y2oVgI0TqUpGSR9s5KCnamAKANMBJ77YXE/H4kWmPTpQmHw+GpaEpPT+fEiRPVKprCw8O95s249/5xbUViO9PD5Ok1slYbiqvcK1Xt+YrjZU4zeY6ieq+qHajx8wSgTrpgOmlDvMJPJ12IVGB2EKqqcujQIVatWkVRkWuoNCEhgUmTJhEREdEs7ynhpg4SboQQlZlzCjj63hpyVu0BFRSdhpjpI4i9biz64HMvk1ZVlZycHNLT08nIyCAzMxOr1XsJh6CgIK8w05K/m9wLQuY5CjllLyTPXshpRyGn7UWcthd6jp9t2Qm3AI2pIvyEEK4NrtQL5ApE4boQ6cluR2w2G1u2bGHLli04HA7PwoBjxozBaGzaoCvhpg4SboQQAPaScjI/+ZnjX21Btbl6TiLH9ydh1kWYOjd+kqSqquTn53t6ZjIyMjzrhbj5+fkRFxdHQkIC8fHxhIWFteqiAVVVKVctnLYXum4O17959iLP/dP2wnpXs/kpRjrpzgQg9/1O2hDCK3qE/FvBekai/goKCli5cqVn5evg4GBuu+22Ji0bb8jnd8daJ10I0eE5rXayvttGxkfrsRe7QkfIwAQS50wmqFfjNg0sLi72CjPubno3vV5fraKpLX1wK4qCv2LC32Ai1lD7PlllTjN59qIqvUBF5NkrHjsKKXWaKVctHLPlcMyWU+u1jIrB1etTqcfHNfQV6hkSC5BK0lYjNDSUGTNmkJKSwsqVK+ndu7fP1sMB6bnxdXOEEC1EdTrJXfcb6UtWYTlZAIB/fCSJt04mbHjPBn1IlpeXe9aZOXr0KHl53huXarVaYmJiPD0zXbp0abN79DQ1s9NSrcfHHYBOO4o4bS+g5Cz73rkZFb0n9FQe+uqkC/UcC9L4SwBqYXa73bPwX1OSnhshhKikYE8aaW8vp+RwFgCGTkHE3zSB6EmDz1oBBWC1Wjl27Jhn8byTJ09WO6dz586eMNOtW7cWKY1ti0waIzGGSGKIrPUci9NKnqPIM/R1ynFmLlBexVygImcpFtVGlu0UWbZTtV5Lr+jOzP3xTIA+Mxm6U0UAauyq1aI6X/bYeNrg6wYIIURzKU0/Sdo7K8nfdhgArb+BbldfQNcrfofWVHtZs8Ph4Pjx456emRMnTuB0eq9hExER4Zk3ExcXh8kkk2SbilFjoIsmgi762qturE6ba4d7T4/PmflA7p6hQkcJNtXu2t7GnlfrtXRoXYHHM+/nTP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512"><!-- Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> </a> </div> </div> </div> </footer> </div> <div class=md-dialog data-md-component=dialog> <div class="md-dialog__inner md-typeset"></div> </div> <script id=__config type=application/json>{"base": "../..", "features": ["announce.dismiss", "content.action.edit", "content.action.view", "content.code.annotate", "content.code.copy", "content.tooltips", "navigation.footer", "navigation.indexes", "navigation.sections", "navigation.tabs", "navigation.top", "navigation.tracking", "search.highlight", "search.share", "search.suggest", "toc.follow"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", 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\ No newline at end of file
diff --git a/0.15.7/examples/example-3-2d-ordered-state/index.html b/0.15.7/examples/example-3-2d-ordered-state/index.html
index 7ca3b5f..8899a5f 100644
--- a/0.15.7/examples/example-3-2d-ordered-state/index.html
+++ b/0.15.7/examples/example-3-2d-ordered-state/index.html
@@ -386,7 +386,7 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=eabc353e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-3-2d-ordered-state.py> Download Script </a> <a class=md-button download href=../../assets/data/striated-phase-hardware.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0070f717"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=2d-state-preparation>2D State Preparation<a class=anchor-link href=#2d-state-preparation>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to create the Striated Phase on a 2D chain of atoms.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6b1111cc"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>You might notice that the tools we need to import are a lot shorter than prior instances. This is because we're taking advantage of bloqade Python's built-in visualization capabilities instead of crafting a new plot with matplotlib.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d6d6a354"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade.atom_arrangement</span> <span class=kn>import</span> <span class=n>Square</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=664e9ad7"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-3-2d-ordered-state.py> Download Script </a> <a class=md-button download href=../../assets/data/striated-phase-hardware.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1ede33f8"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=2d-state-preparation>2D State Preparation<a class=anchor-link href=#2d-state-preparation>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to create the Striated Phase on a 2D chain of atoms.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=98901342"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>You might notice that the tools we need to import are a lot shorter than prior instances. This is because we're taking advantage of bloqade Python's built-in visualization capabilities instead of crafting a new plot with matplotlib.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d3bb5203"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade.atom_arrangement</span> <span class=kn>import</span> <span class=n>Square</span>
 <span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>from</span> <span class=nn>bokeh.io</span> <span class=kn>import</span> <span class=n>output_notebook</span>
 
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-var element = document.getElementById('0d3e300b-8cbe-4c10-a732-cea2f352f9f9');
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+var element = document.getElementById('8842ad79-cc06-4a53-8c90-74380cbe9da0');
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@@ -564,7 +564,7 @@
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-    const el = document.getElementById("b1f425a3-d58b-430e-80bc-4f0bda0ad50d");
+    const el = document.getElementById("a9811501-b855-41f5-bd99-e76def82ac85");
     if (el != null) {
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@@ -670,7 +670,7 @@
       console.log("Bokeh: BokehJS failed to load within specified timeout.");
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     } else if (force !== true) {
-      const cell = $(document.getElementById("b1f425a3-d58b-430e-80bc-4f0bda0ad50d")).parents('.cell').data().cell;
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@@ -685,7 +685,7 @@
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   }
 }(window));
-</script> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=269f19ec"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=program-definition>Program Definition<a class=anchor-link href=#program-definition>¶</a></h2><p>We define a program where our geometry is a square lattice of 3x3 atoms. Notice that unlke the 1D Z2 state preparation example the detuning now ramps to a higher value and the atoms are closer together.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ecaddaae"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># Have atoms separated by 5.9 micrometers</span>
+</script> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=84366c0f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=program-definition>Program Definition<a class=anchor-link href=#program-definition>¶</a></h2><p>We define a program where our geometry is a square lattice of 3x3 atoms. Notice that unlke the 1D Z2 state preparation example the detuning now ramps to a higher value and the atoms are closer together.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8bd032a9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># Have atoms separated by 5.9 micrometers</span>
 <span class=n>L</span> <span class=o>=</span> <span class=mi>3</span>
 <span class=n>lattice_spacing</span> <span class=o>=</span> <span class=mf>5.9</span>
 
@@ -700,7 +700,7 @@
 <span class=p>)</span>
 
 <span class=n>batch</span> <span class=o>=</span> <span class=n>prog</span><span class=o>.</span><span class=n>assign</span><span class=p>(</span><span class=n>delta_end</span><span class=o>=</span><span class=mf>42.66</span><span class=p>,</span> <span class=n>sweep_time</span><span class=o>=</span><span class=mf>2.4</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2># Have atoms separated by 5.9 micrometers L = 3 lattice_spacing = 5.9 rabi_amplitude_values = [0.0, 15.8, 15.8, 0.0] rabi_detuning_values = [-16.33, -16.33, "delta_end", "delta_end"] durations = [0.8, "sweep_time", 0.8] prog = ( Square(L, lattice_spacing=lattice_spacing) .rydberg.rabi.amplitude.uniform.piecewise_linear(durations, rabi_amplitude_values) .detuning.uniform.piecewise_linear(durations, rabi_detuning_values) ) batch = prog.assign(delta_end=42.66, sweep_time=2.4)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=be367689"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=submitting-to-emulator-and-hardware>Submitting to Emulator and Hardware<a class=anchor-link href=#submitting-to-emulator-and-hardware>¶</a></h2><p>Just as in prior examples, we submit our program to both hardware and the emulator and save the intermediate data in files for convenient fetching when the results are ready from hardware, as well as avoiding having to repeat emulation runs for the purposes of analysis.</p> <p>Considering how small a 3 x 3 lattice of atoms is relative to machine capabilities, we also take advantage of parallelization to duplicate the geometry and get more data per shot when submitting to Hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=2e4ab760"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2># Have atoms separated by 5.9 micrometers L = 3 lattice_spacing = 5.9 rabi_amplitude_values = [0.0, 15.8, 15.8, 0.0] rabi_detuning_values = [-16.33, -16.33, "delta_end", "delta_end"] durations = [0.8, "sweep_time", 0.8] prog = ( Square(L, lattice_spacing=lattice_spacing) .rydberg.rabi.amplitude.uniform.piecewise_linear(durations, rabi_amplitude_values) .detuning.uniform.piecewise_linear(durations, rabi_detuning_values) ) batch = prog.assign(delta_end=42.66, sweep_time=2.4)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f1ab4034"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=submitting-to-emulator-and-hardware>Submitting to Emulator and Hardware<a class=anchor-link href=#submitting-to-emulator-and-hardware>¶</a></h2><p>Just as in prior examples, we submit our program to both hardware and the emulator and save the intermediate data in files for convenient fetching when the results are ready from hardware, as well as avoiding having to repeat emulation runs for the purposes of analysis.</p> <p>Considering how small a 3 x 3 lattice of atoms is relative to machine capabilities, we also take advantage of parallelization to duplicate the geometry and get more data per shot when submitting to Hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b7057493"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
     <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"striated-phase-emulation.json"</span>
 <span class=p>)</span>
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>):</span>
@@ -711,20 +711,20 @@
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>hw_filename</span><span class=p>):</span>
     <span class=n>future</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>100</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>future</span><span class=p>,</span> <span class=n>hw_filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3>emu_filename = os.path.join( os.path.abspath(""), "data", "striated-phase-emulation.json" ) if not os.path.isfile(emu_filename): emu_future = batch.braket.local_emulator().run(shots=10000) save(emu_future, emu_filename) hw_filename = os.path.join(os.path.abspath(""), "data", "striated-phase-hardware.json") if not os.path.isfile(hw_filename): future = batch.parallelize(24).braket.aquila().run_async(shots=100) save(future, hw_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=dc56d7e9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=extracting-results>Extracting Results<a class=anchor-link href=#extracting-results>¶</a></h2><p>We can reload our files to get results:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=7f573add"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># retrieve results from emulator and HW</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3>emu_filename = os.path.join( os.path.abspath(""), "data", "striated-phase-emulation.json" ) if not os.path.isfile(emu_filename): emu_future = batch.braket.local_emulator().run(shots=10000) save(emu_future, emu_filename) hw_filename = os.path.join(os.path.abspath(""), "data", "striated-phase-hardware.json") if not os.path.isfile(hw_filename): future = batch.parallelize(24).braket.aquila().run_async(shots=100) save(future, hw_filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f0d37e7d"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=extracting-results>Extracting Results<a class=anchor-link href=#extracting-results>¶</a></h2><p>We can reload our files to get results:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=45285ca6"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># retrieve results from emulator and HW</span>
 <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>hw_filename</span><span class=p>)</span>
 
 <span class=c1># Uncomment lines below to fetch results from Braket</span>
 <span class=c1># hardware_batch = hardware_batch.fetch()</span>
 <span class=c1># save(hardware_batch, filename)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4># retrieve results from emulator and HW emu_batch = load(emu_filename) hardware_batch = load(hw_filename) # Uncomment lines below to fetch results from Braket # hardware_batch = hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d930b84a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=visualizing-results-with-ease>Visualizing Results With Ease<a class=anchor-link href=#visualizing-results-with-ease>¶</a></h2><p>In prior examples we've leverage Bloqade's ability to automatically put hardware and emulation results into the formats we need to make analysis easier.</p> <p>Now we'll go one step further by letting Bloqade Python do the visualization for us. In this case we'll visualize the Rydberg Densities of our system overlaid on the original geometry with just the following:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=2725dfcf"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5>emu_batch.report().show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type=text/html tabindex=0> <div data-root-id=p1163 id=eb6cb458-00c5-4dcc-ba4f-c855453b6b2f style="display: contents;"></div> </div> </div> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedJavaScript jp-OutputArea-output" data-mime-type=application/javascript id=498fbdb5-1372-4a48-9dab-501a6b0badf1 tabindex=0> <script type=text/javascript>
-var element = document.getElementById('498fbdb5-1372-4a48-9dab-501a6b0badf1');
+</pre></div> <div class=clipboard-copy-txt id=cell-4># retrieve results from emulator and HW emu_batch = load(emu_filename) hardware_batch = load(hw_filename) # Uncomment lines below to fetch results from Braket # hardware_batch = hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d293dc84"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=visualizing-results-with-ease>Visualizing Results With Ease<a class=anchor-link href=#visualizing-results-with-ease>¶</a></h2><p>In prior examples we've leverage Bloqade's ability to automatically put hardware and emulation results into the formats we need to make analysis easier.</p> <p>Now we'll go one step further by letting Bloqade Python do the visualization for us. In this case we'll visualize the Rydberg Densities of our system overlaid on the original geometry with just the following:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4c4b3784"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5>emu_batch.report().show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type=text/html tabindex=0> <div data-root-id=p1163 id=b195dc6b-7050-42cd-9d16-0d9c3932e1ed style="display: contents;"></div> </div> </div> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedJavaScript jp-OutputArea-output" data-mime-type=application/javascript id=68b2aae8-c763-47e4-8189-7880a244dd34 tabindex=0> <script type=text/javascript>
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-</script> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=8e383012"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Just as before, we let Bloqade generate a <code>report</code> which contains all the results in easy to digest format but we invoke the <code>.show()</code> method of our report which us easily get an idea of the results of our experiment with interactive plots.</p> <p>The plot that mos interests us is the one on the right under the "Rydberg Density" section.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=13321496"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6>hardware_batch.report().show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type=text/html tabindex=0> <div data-root-id=p1326 id=c63fe1a1-a173-4f5b-bbfa-b32a30e242a5 style="display: contents;"></div> </div> </div> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedJavaScript jp-OutputArea-output" data-mime-type=application/javascript id=1ab454e5-44b3-40cd-9ebc-2c7f3079280d tabindex=0> <script type=text/javascript>
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+</script> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=206d8244"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Just as before, we let Bloqade generate a <code>report</code> which contains all the results in easy to digest format but we invoke the <code>.show()</code> method of our report which us easily get an idea of the results of our experiment with interactive plots.</p> <p>The plot that mos interests us is the one on the right under the "Rydberg Density" section.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=a0a98016"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
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     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=7a0818c9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-3-time-sweep.py> Download Script </a> <a class=md-button download href=../../assets/data/time-sweep-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=dbaa6967"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=1d-z2-state-preparation>1D Z2 State Preparation<a class=anchor-link href=#1d-z2-state-preparation>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to create the Z2 ordered phase on a 1D chain of atoms and how to perform a scan over the sweep time to understand the behavior of an adiabatic sweep and the effect of the Rydberg blockade on a many-body system.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=65762c4e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Let's import all the tools we'll need.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3b164c54"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=aab0b60c"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-3-time-sweep.py> Download Script </a> <a class=md-button download href=../../assets/data/time-sweep-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2ddde7a3"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=1d-z2-state-preparation>1D Z2 State Preparation<a class=anchor-link href=#1d-z2-state-preparation>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to create the Z2 ordered phase on a 1D chain of atoms and how to perform a scan over the sweep time to understand the behavior of an adiabatic sweep and the effect of the Rydberg blockade on a many-body system.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=dbb7d8b7"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>Let's import all the tools we'll need.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=f33efd88"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>from</span> <span class=nn>bloqade.atom_arrangement</span> <span class=kn>import</span> <span class=n>Chain</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
 <span class=kn>import</span> <span class=nn>os</span>
@@ -394,7 +394,7 @@
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>):</span>
     <span class=n>os</span><span class=o>.</span><span class=n>mkdir</span><span class=p>(</span><span class=s2>"data"</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import save, load from bloqade.atom_arrangement import Chain import numpy as np import os import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0d6a6a76"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=program-definition>Program Definition<a class=anchor-link href=#program-definition>¶</a></h2><p>We define a program where our geometry is a chain of 11 atoms with a distance of 6.1 micrometers between atoms.</p> <p>The pulse schedule presented here should be reminiscent of the Two Qubit Adiabatic Sweep example although we've opted to reserve variable usage for values that will actually have their parameters swept.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=be56b75b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># Define relevant parameters for the lattice geometry and pulse schedule</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import save, load from bloqade.atom_arrangement import Chain import numpy as np import os import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data")</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=38115336"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=program-definition>Program Definition<a class=anchor-link href=#program-definition>¶</a></h2><p>We define a program where our geometry is a chain of 11 atoms with a distance of 6.1 micrometers between atoms.</p> <p>The pulse schedule presented here should be reminiscent of the Two Qubit Adiabatic Sweep example although we've opted to reserve variable usage for values that will actually have their parameters swept.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=fc3caaa5"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># Define relevant parameters for the lattice geometry and pulse schedule</span>
 <span class=n>n_atoms</span> <span class=o>=</span> <span class=mi>11</span>
 <span class=n>lattice_spacing</span> <span class=o>=</span> <span class=mf>6.1</span>
 <span class=n>min_time_step</span> <span class=o>=</span> <span class=mf>0.05</span>
@@ -419,7 +419,7 @@
 <span class=n>time_sweep_z2_job</span> <span class=o>=</span> <span class=n>time_sweep_z2_prog</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span>
     <span class=n>sweep_time</span><span class=o>=</span><span class=n>np</span><span class=o>.</span><span class=n>linspace</span><span class=p>(</span><span class=n>min_time_step</span><span class=p>,</span> <span class=mf>2.4</span><span class=p>,</span> <span class=mi>20</span><span class=p>)</span>
 <span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2> # Define relevant parameters for the lattice geometry and pulse schedule n_atoms = 11 lattice_spacing = 6.1 min_time_step = 0.05 # Define Rabi amplitude and detuning values. # Note the addition of a "sweep_time" variable # for performing sweeps of time values. rabi_amplitude_values = [0.0, 15.8, 15.8, 0.0] rabi_detuning_values = [-16.33, -16.33, 16.33, 16.33] durations = [0.8, "sweep_time", 0.8] time_sweep_z2_prog = ( Chain(n_atoms, lattice_spacing=lattice_spacing) .rydberg.rabi.amplitude.uniform.piecewise_linear(durations, rabi_amplitude_values) .detuning.uniform.piecewise_linear(durations, rabi_detuning_values) ) # Allow "sweep_time" to assume values from 0.05 to 2.4 microseconds for a total of # 20 possible values. # Starting at exactly 0.0 isn't feasible so we use the `min_time_step` defined # previously. time_sweep_z2_job = time_sweep_z2_prog.batch_assign( sweep_time=np.linspace(min_time_step, 2.4, 20) )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=79feaa90"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=running-on-the-emulator-and-hardware>Running on the Emulator and Hardware<a class=anchor-link href=#running-on-the-emulator-and-hardware>¶</a></h2><p>With our program properly composed we can now easily send it off to both the emulator and hardware.</p> <p>We select the Braket emulator and tell it that for each variation of the "time_sweep" variable we'd like to run 10000 shots. For the hardware we take advantage of the fact that 11 atoms takes up so little space on the machine we can duplicate that geometry multiple times to get more data per shot. We set a distance of 24 micrometers between copies to minimize potential interactions between them.</p> <p>For both cases, to allow us to submit our program without having to wait on immediate results from hardware (which could take a while considering queueing and window restrictions), we save the necessary metadata to a file that can then be reloaded later and results fetched when they are available.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=432a1864"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"time-sweep-emulation.json"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2> # Define relevant parameters for the lattice geometry and pulse schedule n_atoms = 11 lattice_spacing = 6.1 min_time_step = 0.05 # Define Rabi amplitude and detuning values. # Note the addition of a "sweep_time" variable # for performing sweeps of time values. rabi_amplitude_values = [0.0, 15.8, 15.8, 0.0] rabi_detuning_values = [-16.33, -16.33, 16.33, 16.33] durations = [0.8, "sweep_time", 0.8] time_sweep_z2_prog = ( Chain(n_atoms, lattice_spacing=lattice_spacing) .rydberg.rabi.amplitude.uniform.piecewise_linear(durations, rabi_amplitude_values) .detuning.uniform.piecewise_linear(durations, rabi_detuning_values) ) # Allow "sweep_time" to assume values from 0.05 to 2.4 microseconds for a total of # 20 possible values. # Starting at exactly 0.0 isn't feasible so we use the `min_time_step` defined # previously. time_sweep_z2_job = time_sweep_z2_prog.batch_assign( sweep_time=np.linspace(min_time_step, 2.4, 20) )</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=08d7fec9"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=running-on-the-emulator-and-hardware>Running on the Emulator and Hardware<a class=anchor-link href=#running-on-the-emulator-and-hardware>¶</a></h2><p>With our program properly composed we can now easily send it off to both the emulator and hardware.</p> <p>We select the Braket emulator and tell it that for each variation of the "time_sweep" variable we'd like to run 10000 shots. For the hardware we take advantage of the fact that 11 atoms takes up so little space on the machine we can duplicate that geometry multiple times to get more data per shot. We set a distance of 24 micrometers between copies to minimize potential interactions between them.</p> <p>For both cases, to allow us to submit our program without having to wait on immediate results from hardware (which could take a while considering queueing and window restrictions), we save the necessary metadata to a file that can then be reloaded later and results fetched when they are available.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=521ca176"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"time-sweep-emulation.json"</span><span class=p>)</span>
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>):</span>
     <span class=n>emu_future</span> <span class=o>=</span> <span class=n>time_sweep_z2_job</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>local_emulator</span><span class=p>()</span><span class=o>.</span><span class=n>run</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>10000</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>emu_future</span><span class=p>,</span> <span class=n>emu_filename</span><span class=p>)</span>
@@ -428,7 +428,7 @@
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>filename</span><span class=p>):</span>
     <span class=n>future</span> <span class=o>=</span> <span class=n>time_sweep_z2_job</span><span class=o>.</span><span class=n>parallelize</span><span class=p>(</span><span class=mi>24</span><span class=p>)</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>100</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>future</span><span class=p>,</span> <span class=n>filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3> emu_filename = os.path.join(os.path.abspath(""), "data", "time-sweep-emulation.json") if not os.path.isfile(emu_filename): emu_future = time_sweep_z2_job.braket.local_emulator().run(shots=10000) save(emu_future, emu_filename) filename = os.path.join(os.path.abspath(""), "data", "time-sweep-job.json") if not os.path.isfile(filename): future = time_sweep_z2_job.parallelize(24).braket.aquila().run_async(shots=100) save(future, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2e6bcd00"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>To make our lives easier we define a trivial function to extract the probability of the Z2 phase from each of the tasks generated from the parameter sweep. The counts are obtained from the <code>report</code>of the batch object.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=83ee644f"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>get_z2_probabilities</span><span class=p>(</span><span class=n>report</span><span class=p>):</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3> emu_filename = os.path.join(os.path.abspath(""), "data", "time-sweep-emulation.json") if not os.path.isfile(emu_filename): emu_future = time_sweep_z2_job.braket.local_emulator().run(shots=10000) save(emu_future, emu_filename) filename = os.path.join(os.path.abspath(""), "data", "time-sweep-job.json") if not os.path.isfile(filename): future = time_sweep_z2_job.parallelize(24).braket.aquila().run_async(shots=100) save(future, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=fc32e3af"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the Results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>To make our lives easier we define a trivial function to extract the probability of the Z2 phase from each of the tasks generated from the parameter sweep. The counts are obtained from the <code>report</code>of the batch object.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=46135fe1"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>get_z2_probabilities</span><span class=p>(</span><span class=n>report</span><span class=p>):</span>
     <span class=n>z2_probabilities</span> <span class=o>=</span> <span class=p>[]</span>
 
     <span class=k>for</span> <span class=n>count</span> <span class=ow>in</span> <span class=n>report</span><span class=o>.</span><span class=n>counts</span><span class=p>():</span>
@@ -436,14 +436,14 @@
         <span class=n>z2_probabilities</span><span class=o>.</span><span class=n>append</span><span class=p>(</span><span class=n>z2_probability</span><span class=p>)</span>
 
     <span class=k>return</span> <span class=n>z2_probabilities</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4> def get_z2_probabilities(report): z2_probabilities = [] for count in report.counts(): z2_probability = count["01010101010"] / sum(list(count.values())) z2_probabilities.append(z2_probability) return z2_probabilities</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4c1b5047"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=extracting-counts-and-probabilities>Extracting Counts And Probabilities<a class=anchor-link href=#extracting-counts-and-probabilities>¶</a></h2><p>We will now extract the counts and probabilities from the emulator and hardware runs. We will then plot the results. First we load the data from the files:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=881aaa01"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># retrieve results from HW</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4> def get_z2_probabilities(report): z2_probabilities = [] for count in report.counts(): z2_probability = count["01010101010"] / sum(list(count.values())) z2_probabilities.append(z2_probability) return z2_probabilities</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d2adbaea"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=extracting-counts-and-probabilities>Extracting Counts And Probabilities<a class=anchor-link href=#extracting-counts-and-probabilities>¶</a></h2><p>We will now extract the counts and probabilities from the emulator and hardware runs. We will then plot the results. First we load the data from the files:</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=82d24e02"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=c1># retrieve results from HW</span>
 <span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>filename</span><span class=p>)</span>
 
 <span class=c1># Uncomment lines below to fetch results from Braket</span>
 <span class=c1># hardware_batch = hardware_batch.fetch()</span>
 <span class=c1># save(hardware_batch, filename)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5># retrieve results from HW emu_batch = load(emu_filename) hardware_batch = load(filename) # Uncomment lines below to fetch results from Braket # hardware_batch = hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=5e8a1b7c"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>To get the counts we need to get a report from the batch objects. Then with the report we can get the counts. The counts are a dictionary that maps the bitstring to the number of times that bitstring was measured.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=0c3d6e91"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5># retrieve results from HW emu_batch = load(emu_filename) hardware_batch = load(filename) # Uncomment lines below to fetch results from Braket # hardware_batch = hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f6cd4507"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>To get the counts we need to get a report from the batch objects. Then with the report we can get the counts. The counts are a dictionary that maps the bitstring to the number of times that bitstring was measured.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4613c124"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>emu_probabilities</span> <span class=o>=</span> <span class=n>get_z2_probabilities</span><span class=p>(</span><span class=n>emu_report</span><span class=p>)</span>
 <span class=n>hardware_probabilities</span> <span class=o>=</span> <span class=n>get_z2_probabilities</span><span class=p>(</span><span class=n>hardware_report</span><span class=p>)</span>
@@ -457,4 +457,4 @@
 
 <span class=n>plt</span><span class=o>.</span><span class=n>legend</span><span class=p>()</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-6>emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_probabilities = get_z2_probabilities(emu_report) hardware_probabilities = get_z2_probabilities(hardware_report) emu_sweep_times = emu_report.list_param("sweep_time") hardware_sweep_times = hardware_report.list_param("sweep_time") plt.plot(emu_sweep_times, emu_probabilities, label="Emulator", color="#878787") plt.plot(hardware_sweep_times, hardware_probabilities, label="QPU", color="#6437FF") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=bf5e7ecd"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=analysis>Analysis<a class=anchor-link href=#analysis>¶</a></h1><p>As expected, we see that if we allow the pulse schedule to run for a longer and longer period of time (more "adiabatically") we have an increasing probability of creating the Z2 phase.</p> </div> </div> </div> </div> </div> <!-- jp-Notebook --> </div> <!-- jupyter-wrapper --> </article> </div> <script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script> </div> <button type=button class="md-top 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+</pre></div> <div class=clipboard-copy-txt id=cell-6>emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_probabilities = get_z2_probabilities(emu_report) hardware_probabilities = get_z2_probabilities(hardware_report) emu_sweep_times = emu_report.list_param("sweep_time") hardware_sweep_times = hardware_report.list_param("sweep_time") plt.plot(emu_sweep_times, emu_probabilities, label="Emulator", color="#878787") plt.plot(hardware_sweep_times, hardware_probabilities, label="QPU", color="#6437FF") plt.legend() plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d966fb3c"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=analysis>Analysis<a class=anchor-link href=#analysis>¶</a></h1><p>As expected, we see that if we allow the pulse schedule to run for a longer and longer period of time (more "adiabatically") we have an increasing probability of creating the Z2 phase.</p> </div> </div> </div> </div> </div> <!-- jp-Notebook --> </div> <!-- jupyter-wrapper --> </article> </div> <script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script> </div> <button type=button class="md-top 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8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> </a> <a href=https://pypi.org/project/bloqade/ target=_blank rel=noopener title=pypi.org class=md-social__link> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 448 512"><!-- Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 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diff --git a/0.15.7/examples/example-4-quantum-scar-dynamics/index.html b/0.15.7/examples/example-4-quantum-scar-dynamics/index.html
index 6175a3e..46eec59 100644
--- a/0.15.7/examples/example-4-quantum-scar-dynamics/index.html
+++ b/0.15.7/examples/example-4-quantum-scar-dynamics/index.html
@@ -386,7 +386,7 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a3b69c56"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-4-quantum-scar-dynamics.py> Download Script </a> <a class=md-button download href=../../assets/data/quantum-scar-dynamics-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=848ad0b2"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=quantum-scar-dynamics>Quantum Scar Dynamics<a class=anchor-link href=#quantum-scar-dynamics>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to use Bloqade to run a quantum scar dynamics experiment. The protocol is as follows: We first prepare the atoms in a Z2 state using an adiabatic sweep. We then apply a Rabi pulse to the atoms, which will cause the atoms to oscillate but because of the Blockade effect, the atoms will not be able to transition to the Rydberg state. However, the atoms will still oscillate between the ground and some other excited many-body states.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=79b05365"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program<a class=anchor-link href=#define-the-program>¶</a></h2><p>This notebook will also show some advanced features of Bloqade, in particular, how to use the <code>slice</code> and <code>record</code> API to build a program that is compatible with the hardware constraints that the rabi drive must be 0 at the end of the protocol.</p> <p>The idea is that first we define the full waveform we would like to apply to the atoms then after defining the full waveform you simply call the <code>slice</code> method to slice the that waveform stopping at a variable time <code>run_time</code>. This works fine for detuning but for the Rabi drive, we need to make sure that the Rabi drive is 0 at the end of the the waveform. To do this, we use the <code>record</code> method to record the value of the Rabi drive at the end of the waveform. We then use the <code>linear</code> method to append a segment to the waveform that fixes the value of the Rabi drive to 0 at the end of the waveform. Now any value of <code>run_time</code> will be a valid waveform that is compatcible with the hardware constraints.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=20cb233a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>var</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ee25c761"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-4-quantum-scar-dynamics.py> Download Script </a> <a class=md-button download href=../../assets/data/quantum-scar-dynamics-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4e3c5274"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=quantum-scar-dynamics>Quantum Scar Dynamics<a class=anchor-link href=#quantum-scar-dynamics>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this example we show how to use Bloqade to run a quantum scar dynamics experiment. The protocol is as follows: We first prepare the atoms in a Z2 state using an adiabatic sweep. We then apply a Rabi pulse to the atoms, which will cause the atoms to oscillate but because of the Blockade effect, the atoms will not be able to transition to the Rydberg state. However, the atoms will still oscillate between the ground and some other excited many-body states.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=67f8f7ca"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the program<a class=anchor-link href=#define-the-program>¶</a></h2><p>This notebook will also show some advanced features of Bloqade, in particular, how to use the <code>slice</code> and <code>record</code> API to build a program that is compatible with the hardware constraints that the rabi drive must be 0 at the end of the protocol.</p> <p>The idea is that first we define the full waveform we would like to apply to the atoms then after defining the full waveform you simply call the <code>slice</code> method to slice the that waveform stopping at a variable time <code>run_time</code>. This works fine for detuning but for the Rabi drive, we need to make sure that the Rabi drive is 0 at the end of the the waveform. To do this, we use the <code>record</code> method to record the value of the Rabi drive at the end of the waveform. We then use the <code>linear</code> method to append a segment to the waveform that fixes the value of the Rabi drive to 0 at the end of the waveform. Now any value of <code>run_time</code> will be a valid waveform that is compatcible with the hardware constraints.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=968ca297"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>var</span><span class=p>,</span> <span class=n>save</span><span class=p>,</span> <span class=n>load</span>
 <span class=kn>from</span> <span class=nn>bloqade.atom_arrangement</span> <span class=kn>import</span> <span class=n>Chain</span>
 <span class=kn>import</span> <span class=nn>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
@@ -426,7 +426,7 @@
 <span class=n>run_times</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>unique</span><span class=p>(</span><span class=n>np</span><span class=o>.</span><span class=n>hstack</span><span class=p>((</span><span class=n>prep_times</span><span class=p>,</span> <span class=n>scar_times</span><span class=p>)))</span>
 
 <span class=n>batch</span> <span class=o>=</span> <span class=n>quantum_scar_program</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>run_time</span><span class=o>=</span><span class=n>run_times</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1> from bloqade import var, save, load from bloqade.atom_arrangement import Chain import matplotlib.pyplot as plt import numpy as np import os if not os.path.isdir("data"): os.mkdir("data") n_atoms = 11 lattice_spacing = 6.1 run_time = var("run_time") quantum_scar_program = ( Chain(n_atoms, lattice_spacing=lattice_spacing) # define detuning waveform .rydberg.detuning.uniform.piecewise_linear( [0.3, 1.6, 0.3], [-18.8, -18.8, 16.3, 16.3] ) .piecewise_linear([0.2, 1.6], [16.3, 0.0, 0.0]) # slice the detuning waveform .slice(start=0, stop=run_time) # define rabi waveform .amplitude.uniform.piecewise_linear([0.3, 1.6, 0.3], [0.0, 15.7, 15.7, 0.0]) .piecewise_linear([0.2, 1.4, 0.2], [0, 15.7, 15.7, 0]) # slice waveform, add padding for the linear segment .slice(start=0, stop=run_time - 0.065) # record the value of the waveform at the end of the slice to "rabi_value" .record("rabi_value") # append segment to waveform that fixes the value of the waveform to 0 # at the end of the waveform .linear("rabi_value", 0, 0.065) ) # get run times via the following: prep_times = np.arange(0.2, 2.2, 0.2) scar_times = np.arange(2.2, 4.01, 0.01) run_times = np.unique(np.hstack((prep_times, scar_times))) batch = quantum_scar_program.batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3d3f6ccc"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-on-emulator-and-hardware>Run on Emulator and Hardware<a class=anchor-link href=#run-on-emulator-and-hardware>¶</a></h2><p>We will run the experiment on the emulator and hardware, saving the results to disk so that we can plot them later. for more details on where these lines of code come from, see the first few tutorials.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=123fe1e7"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1> from bloqade import var, save, load from bloqade.atom_arrangement import Chain import matplotlib.pyplot as plt import numpy as np import os if not os.path.isdir("data"): os.mkdir("data") n_atoms = 11 lattice_spacing = 6.1 run_time = var("run_time") quantum_scar_program = ( Chain(n_atoms, lattice_spacing=lattice_spacing) # define detuning waveform .rydberg.detuning.uniform.piecewise_linear( [0.3, 1.6, 0.3], [-18.8, -18.8, 16.3, 16.3] ) .piecewise_linear([0.2, 1.6], [16.3, 0.0, 0.0]) # slice the detuning waveform .slice(start=0, stop=run_time) # define rabi waveform .amplitude.uniform.piecewise_linear([0.3, 1.6, 0.3], [0.0, 15.7, 15.7, 0.0]) .piecewise_linear([0.2, 1.4, 0.2], [0, 15.7, 15.7, 0]) # slice waveform, add padding for the linear segment .slice(start=0, stop=run_time - 0.065) # record the value of the waveform at the end of the slice to "rabi_value" .record("rabi_value") # append segment to waveform that fixes the value of the waveform to 0 # at the end of the waveform .linear("rabi_value", 0, 0.065) ) # get run times via the following: prep_times = np.arange(0.2, 2.2, 0.2) scar_times = np.arange(2.2, 4.01, 0.01) run_times = np.unique(np.hstack((prep_times, scar_times))) batch = quantum_scar_program.batch_assign(run_time=run_times)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=536f6886"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-on-emulator-and-hardware>Run on Emulator and Hardware<a class=anchor-link href=#run-on-emulator-and-hardware>¶</a></h2><p>We will run the experiment on the emulator and hardware, saving the results to disk so that we can plot them later. for more details on where these lines of code come from, see the first few tutorials.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=00d25d1a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span>
     <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"quantum-scar-dynamics-emulation.json"</span>
 <span class=p>)</span>
 
@@ -446,11 +446,11 @@
     <span class=n>save</span><span class=p>(</span><span class=n>hardware_batch</span><span class=p>,</span> <span class=n>filename</span><span class=p>)</span>
 </pre></div> <div class=clipboard-copy-txt id=cell-2> emu_filename = os.path.join( os.path.abspath(""), "data", "quantum-scar-dynamics-emulation.json" ) if not os.path.isfile(emu_filename): emu_batch = batch.braket.local_emulator().run(1000) save(emu_batch, emu_filename) filename = os.path.join(os.path.abspath(""), "data", "quantum-scar-dynamics-job.json") if not os.path.isfile(filename): hardware_batch = ( batch.parallelize(24) .braket.aquila() .run_async(100, ignore_submission_error=True) ) save(hardware_batch, filename)</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedText jp-OutputArea-output" data-mime-type=application/vnd.jupyter.stderr tabindex=0> <pre>/home/runner/work/bloqade-python-examples/bloqade-python-examples/.venv/lib/python3.11/site-packages/braket/analog_hamiltonian_simulator/rydberg/rydberg_simulator_helpers.py:632: UserWarning: The solver uses intermediate time value that is larger than the maximum time value specified. The final time value of the specified range is used as an approximation.
   warnings.warn(
-</pre> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=31982556"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>The quantity we are interested in is the probability of the atoms being in the Z2 state. We can get this by looking at the counts of the Z2 state in the report Below we define a function that will get the probability of the Z2 state for each time step in the experiment.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=43fd46c5"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
+</pre> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=fee1c966"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plotting-the-results>Plotting the results<a class=anchor-link href=#plotting-the-results>¶</a></h2><p>The quantity we are interested in is the probability of the atoms being in the Z2 state. We can get this by looking at the counts of the Z2 state in the report Below we define a function that will get the probability of the Z2 state for each time step in the experiment.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=037ce4a0"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>emu_filename</span><span class=p>)</span>
 <span class=n>hardware_batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>filename</span><span class=p>)</span>
 <span class=c1># hardware_batch.fetch()</span>
 <span class=c1># save(hardware_batch, filename)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3> emu_batch = load(emu_filename) hardware_batch = load(filename) # hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=adf3beed"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>get_z2_probabilities</span><span class=p>(</span><span class=n>report</span><span class=p>):</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3> emu_batch = load(emu_filename) hardware_batch = load(filename) # hardware_batch.fetch() # save(hardware_batch, filename)</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=5f218ed0"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>def</span> <span class=nf>get_z2_probabilities</span><span class=p>(</span><span class=n>report</span><span class=p>):</span>
     <span class=n>z2_probabilities</span> <span class=o>=</span> <span class=p>[]</span>
 
     <span class=k>for</span> <span class=n>count</span> <span class=ow>in</span> <span class=n>report</span><span class=o>.</span><span class=n>counts</span><span class=p>():</span>
@@ -458,7 +458,7 @@
         <span class=n>z2_probabilities</span><span class=o>.</span><span class=n>append</span><span class=p>(</span><span class=n>z2_probability</span><span class=p>)</span>
 
     <span class=k>return</span> <span class=n>z2_probabilities</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4>def get_z2_probabilities(report): z2_probabilities = [] for count in report.counts(): z2_probability = count.get("01010101010", 0) / sum(list(count.values())) z2_probabilities.append(z2_probability) return z2_probabilities</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f9f6217a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>We can now plot the results from the emulator and hardware. We see that the emulator</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=df8f32f4"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4>def get_z2_probabilities(report): z2_probabilities = [] for count in report.counts(): z2_probability = count.get("01010101010", 0) / sum(list(count.values())) z2_probabilities.append(z2_probability) return z2_probabilities</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=afe68c27"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>We can now plot the results from the emulator and hardware. We see that the emulator</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=03e42db5"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>emu_report</span> <span class=o>=</span> <span class=n>emu_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 <span class=n>hardware_report</span> <span class=o>=</span> <span class=n>hardware_batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 
 <span class=n>emu_run_times</span> <span class=o>=</span> <span class=n>emu_report</span><span class=o>.</span><span class=n>list_param</span><span class=p>(</span><span class=s2>"run_time"</span><span class=p>)</span>
@@ -474,4 +474,4 @@
 <span class=n>plt</span><span class=o>.</span><span class=n>xlabel</span><span class=p>(</span><span class=s2>"Time ($\mu s$)"</span><span class=p>)</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>ylabel</span><span class=p>(</span><span class=s2>"Z2-state Probability"</span><span class=p>)</span>
 <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5> emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_run_times = emu_report.list_param("run_time") emu_z2_prob = get_z2_probabilities(emu_report) hw_run_times = hardware_report.list_param("run_time") hw_z2_prob = get_z2_probabilities(hardware_report) plt.plot(emu_run_times, emu_z2_prob, label="Emulator", color="#878787") plt.plot(hw_run_times, hw_z2_prob, label="QPU", color="#6437FF") plt.legend() plt.xlabel("Time ($\mu s$)") plt.ylabel("Z2-state Probability") plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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"> 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+</pre></div> <div class=clipboard-copy-txt id=cell-5> emu_report = emu_batch.report() hardware_report = hardware_batch.report() emu_run_times = emu_report.list_param("run_time") emu_z2_prob = get_z2_probabilities(emu_report) hw_run_times = hardware_report.list_param("run_time") hw_z2_prob = get_z2_probabilities(hardware_report) plt.plot(emu_run_times, emu_z2_prob, label="Emulator", color="#878787") plt.plot(hw_run_times, hw_z2_prob, label="QPU", color="#6437FF") plt.legend() plt.xlabel("Time ($\mu s$)") plt.ylabel("Z2-state Probability") plt.show()</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedImage jp-OutputArea-output" tabindex=0> <img alt="No description has been provided for this image" class 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"> 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     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=bfa07f15"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-5-MIS-UDG.py> Download Script </a> <a class=md-button download href=../../assets/data/MIS-UDG-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=e3ce9c0b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=solving-the-maximal-independent-set-problem-on-defective-king-graph>Solving the Maximal Independent Set Problem on defective King Graph<a class=anchor-link href=#solving-the-maximal-independent-set-problem-on-defective-king-graph>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this tutorial, we show how to use some of Bloqade's built-in tools to generate a defects in a graph and then use Bloqade to solve the Maximal Independent Set (MIS) problem on a Unit Disk Graph (UDG), which is easily expressable on Neutral Atom Hardware via the Rydberg blockade mechanism. We will not cover hybrid quantum- classical algorithms in this tutorial, but instead, we will use a simple parameter scan to find the optimal detuning value for an adiabatic ramp. We will cover hybrid quantum-classical algorithms in a future tutorial.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d9d026b1"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the Program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>To define random defects on any Bloqade geometry, simply call the <code>add_defect_density</code> or <code>add_defect_count</code> methods on the geometry object. The <code>add_defect_density</code> method takes a float between 0 and 1 and uses that as the probability of a site being a defect. The <code>add_defect_count</code> method takes the number of defects to add to the geometry placed in random locations. Both ways take an optional <code>rng</code> argument, a numpy random number generator. If no <code>rng</code> argument is provided, then the default numpy random number generator is used. Using the random number generator allows you to set the seed for reproducibility. After that, defining the pulse sequence is the same as in the previous tutorials.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b72d8964"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>load</span><span class=p>,</span> <span class=n>save</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2294c194"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p><a class=md-button download href=example-5-MIS-UDG.py> Download Script </a> <a class=md-button download href=../../assets/data/MIS-UDG-job.json> Download Job </a></p> <div class="admonition warning"> <p class=admonition-title>Job Files for Complete Examples</p> <p> To be able to run the complete examples without having to submit your program to hardware and wait, you'll need to download the associated job files. These files contain the results of running the program on the quantum hardware. <p>You can download the job files by clicking the "Download Job" button above. You'll then need to place the job file in the <code>data</code> directory that was created for you when you ran the <code>import</code> part of the script (alternatively you can make the directory yourself, it should live at the same level as wherever you put this script).</p> </p> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6a9ef7e5"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h1 id=solving-the-maximal-independent-set-problem-on-defective-king-graph>Solving the Maximal Independent Set Problem on defective King Graph<a class=anchor-link href=#solving-the-maximal-independent-set-problem-on-defective-king-graph>¶</a></h1><h2 id=introduction>Introduction<a class=anchor-link href=#introduction>¶</a></h2><p>In this tutorial, we show how to use some of Bloqade's built-in tools to generate a defects in a graph and then use Bloqade to solve the Maximal Independent Set (MIS) problem on a Unit Disk Graph (UDG), which is easily expressable on Neutral Atom Hardware via the Rydberg blockade mechanism. We will not cover hybrid quantum- classical algorithms in this tutorial, but instead, we will use a simple parameter scan to find the optimal detuning value for an adiabatic ramp. We will cover hybrid quantum-classical algorithms in a future tutorial.</p> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=76281081"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=define-the-program>Define the Program.<a class=anchor-link href=#define-the-program>¶</a></h2><p>To define random defects on any Bloqade geometry, simply call the <code>add_defect_density</code> or <code>add_defect_count</code> methods on the geometry object. The <code>add_defect_density</code> method takes a float between 0 and 1 and uses that as the probability of a site being a defect. The <code>add_defect_count</code> method takes the number of defects to add to the geometry placed in random locations. Both ways take an optional <code>rng</code> argument, a numpy random number generator. If no <code>rng</code> argument is provided, then the default numpy random number generator is used. Using the random number generator allows you to set the seed for reproducibility. After that, defining the pulse sequence is the same as in the previous tutorials.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=2665cc34"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>bloqade</span> <span class=kn>import</span> <span class=n>load</span><span class=p>,</span> <span class=n>save</span>
 <span class=kn>from</span> <span class=nn>bloqade.atom_arrangement</span> <span class=kn>import</span> <span class=n>Square</span>
 <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span>
 <span class=kn>import</span> <span class=nn>os</span>
@@ -410,15 +410,15 @@
 <span class=p>)</span>
 
 <span class=n>mis_udg_job</span> <span class=o>=</span> <span class=n>mis_udg_program</span><span class=o>.</span><span class=n>batch_assign</span><span class=p>(</span><span class=n>final_detuning</span><span class=o>=</span><span class=n>np</span><span class=o>.</span><span class=n>linspace</span><span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>80</span><span class=p>,</span> <span class=mi>41</span><span class=p>))</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import load, save from bloqade.atom_arrangement import Square import numpy as np import os import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data") # setting the seed rng = np.random.default_rng(1234) durations = [0.3, 1.6, 0.3] mis_udg_program = ( Square(15, lattice_spacing=5.0) .apply_defect_density(0.3, rng=rng) .rydberg.rabi.amplitude.uniform.piecewise_linear(durations, [0.0, 15.0, 15.0, 0.0]) .detuning.uniform.piecewise_linear( durations, [-30, -30, "final_detuning", "final_detuning"] ) ) mis_udg_job = mis_udg_program.batch_assign(final_detuning=np.linspace(0, 80, 41))</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=387f1a52"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-on-hardware>Run On Hardware<a class=anchor-link href=#run-on-hardware>¶</a></h2><p>We can't run on our emulators because the program size is too large. Instead we will run on hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=7e421044"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"MIS-UDG-job.json"</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>from bloqade import load, save from bloqade.atom_arrangement import Square import numpy as np import os import matplotlib.pyplot as plt if not os.path.isdir("data"): os.mkdir("data") # setting the seed rng = np.random.default_rng(1234) durations = [0.3, 1.6, 0.3] mis_udg_program = ( Square(15, lattice_spacing=5.0) .apply_defect_density(0.3, rng=rng) .rydberg.rabi.amplitude.uniform.piecewise_linear(durations, [0.0, 15.0, 15.0, 0.0]) .detuning.uniform.piecewise_linear( durations, [-30, -30, "final_detuning", "final_detuning"] ) ) mis_udg_job = mis_udg_program.batch_assign(final_detuning=np.linspace(0, 80, 41))</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4bf607a3"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=run-on-hardware>Run On Hardware<a class=anchor-link href=#run-on-hardware>¶</a></h2><p>We can't run on our emulators because the program size is too large. Instead we will run on hardware.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=31c64b9e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>filename</span> <span class=o>=</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>join</span><span class=p>(</span><span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>abspath</span><span class=p>(</span><span class=s2>""</span><span class=p>),</span> <span class=s2>"data"</span><span class=p>,</span> <span class=s2>"MIS-UDG-job.json"</span><span class=p>)</span>
 
 <span class=k>if</span> <span class=ow>not</span> <span class=n>os</span><span class=o>.</span><span class=n>path</span><span class=o>.</span><span class=n>isfile</span><span class=p>(</span><span class=n>filename</span><span class=p>):</span>
     <span class=n>hw_batch</span> <span class=o>=</span> <span class=n>mis_udg_job</span><span class=o>.</span><span class=n>braket</span><span class=o>.</span><span class=n>aquila</span><span class=p>()</span><span class=o>.</span><span class=n>run_async</span><span class=p>(</span><span class=n>shots</span><span class=o>=</span><span class=mi>100</span><span class=p>)</span>
     <span class=n>save</span><span class=p>(</span><span class=n>hw_batch</span><span class=p>,</span> <span class=n>filename</span><span class=p>)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2>filename = os.path.join(os.path.abspath(""), "data", "MIS-UDG-job.json") if not os.path.isfile(filename): hw_batch = mis_udg_job.braket.aquila().run_async(shots=100) save(hw_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2fccc9ed"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plot-results>Plot Results<a class=anchor-link href=#plot-results>¶</a></h2><p>Here, the total number of Rydberg excitations is plotted as a function of the final detuning. The total number of Rydberg excitations is a proxy for the largest independent set size because the number of violations to the Rydberg blockade is and will not scale with the size of the independent set. We start by loading the results</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=6771aaad"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>filename</span><span class=p>)</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2>filename = os.path.join(os.path.abspath(""), "data", "MIS-UDG-job.json") if not os.path.isfile(filename): hw_batch = mis_udg_job.braket.aquila().run_async(shots=100) save(hw_batch, filename)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=8f05dda0"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <h2 id=plot-results>Plot Results<a class=anchor-link href=#plot-results>¶</a></h2><p>Here, the total number of Rydberg excitations is plotted as a function of the final detuning. The total number of Rydberg excitations is a proxy for the largest independent set size because the number of violations to the Rydberg blockade is and will not scale with the size of the independent set. We start by loading the results</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=4638bf69"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>batch</span> <span class=o>=</span> <span class=n>load</span><span class=p>(</span><span class=n>filename</span><span class=p>)</span>
 <span class=c1># batch.fetch()</span>
 <span class=c1># save(filename, batch)</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3> batch = load(filename) # batch.fetch() # save(filename, batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=dbb3bc8b"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>The report object already has a method to calculate the Rydberg densities. We can use this to calculate the average total Rydberg density for each final detuning. then, we can plot the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=0d516c60"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>report</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3> batch = load(filename) # batch.fetch() # save(filename, batch)</div> </div> </div> </div> </div> </div> </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=fd74d0e6"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type=text/markdown> <p>The report object already has a method to calculate the Rydberg densities. We can use this to calculate the average total Rydberg density for each final detuning. then, we can plot the results.</p> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4ffe426c"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>report</span> <span class=o>=</span> <span class=n>batch</span><span class=o>.</span><span class=n>report</span><span class=p>()</span>
 
 <span class=n>average_rydberg_excitation</span> <span class=o>=</span> <span class=n>report</span><span class=o>.</span><span class=n>rydberg_densities</span><span class=p>(</span><span class=n>filter_perfect_filling</span><span class=o>=</span><span class=kc>False</span><span class=p>)</span><span class=o>.</span><span class=n>sum</span><span class=p>(</span>
     <span class=n>axis</span><span class=o>=</span><span class=mi>1</span>
diff --git a/0.15.7/scripts/gen_ref_nav/index.html b/0.15.7/scripts/gen_ref_nav/index.html
index 385e4b2..1b620a6 100644
--- a/0.15.7/scripts/gen_ref_nav/index.html
+++ b/0.15.7/scripts/gen_ref_nav/index.html
@@ -386,11 +386,11 @@
     display: inline-block;
     white-space: normal;
   }
-</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=db78ffb0"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=sd>"""Generate the code reference pages and navigation."""</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-1>"""Generate the code reference pages and navigation."""</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class="jp-OutputArea-child jp-OutputArea-executeResult"> <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[1]:</div> <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type=text/plain tabindex=0> <pre>'Generate the code reference pages and navigation.'</pre> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=f7e99dda"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>pathlib</span> <span class=kn>import</span> <span class=n>Path</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-2>from pathlib import Path</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=c18485a3"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>import</span> <span class=nn>mkdocs_gen_files</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-3>import mkdocs_gen_files</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=0130ad5e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>SRC_PATH</span> <span class=o>=</span> <span class=s2>"src"</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-4>SRC_PATH = "src"</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=dea52b2e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>skip_keywords</span> <span class=o>=</span> <span class=p>[</span>
+</style> <!-- End of mermaid configuration --><div class=jupyter-wrapper> <div class=jp-Notebook data-jp-theme-light=true data-jp-theme-name="JupyterLab Light"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=c5d4b685"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-1> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=sd>"""Generate the code reference pages and navigation."""</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-1>"""Generate the code reference pages and navigation."""</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class="jp-OutputArea-child jp-OutputArea-executeResult"> <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[1]:</div> <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type=text/plain tabindex=0> <pre>'Generate the code reference pages and navigation.'</pre> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=7212c414"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-2> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>from</span> <span class=nn>pathlib</span> <span class=kn>import</span> <span class=n>Path</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-2>from pathlib import Path</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=838c0f5a"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-3> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=kn>import</span> <span class=nn>mkdocs_gen_files</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-3>import mkdocs_gen_files</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=da6b4e8e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-4> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>SRC_PATH</span> <span class=o>=</span> <span class=s2>"src"</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-4>SRC_PATH = "src"</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=de4a0a30"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-5> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>skip_keywords</span> <span class=o>=</span> <span class=p>[</span>
     <span class=s2>"julia"</span><span class=p>,</span>  <span class=c1>## [KHW] skip for now since we didn't have julia codegen rdy</span>
     <span class=s2>"builder/base"</span><span class=p>,</span>  <span class=c1>## hiding from user</span>
     <span class=s2>"builder/terminate"</span><span class=p>,</span>  <span class=c1>## hiding from user</span>
@@ -404,7 +404,7 @@
     <span class=s2>"submission/capabilities"</span><span class=p>,</span>  <span class=c1>## hiding from user</span>
     <span class=s2>"submission/quera_api_client"</span><span class=p>,</span>
 <span class=p>]</span>
-</pre></div> <div class=clipboard-copy-txt id=cell-5>skip_keywords = [ "julia", ## [KHW] skip for now since we didn't have julia codegen rdy "builder/base", ## hiding from user "builder/terminate", ## hiding from user "ir/tree_print", ## hiding from user "ir/visitor", ## hiding from user "codegen/", ## hiding from user "builder/factory", ## hiding from user "builder_old", ## deprecated from user "task_old", ## deprecated from user "visualization", ## hiding from user "submission/capabilities", ## hiding from user "submission/quera_api_client", ]</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=8c75ae28"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>nav</span> <span class=o>=</span> <span class=n>mkdocs_gen_files</span><span class=o>.</span><span class=n>Nav</span><span class=p>()</span>
+</pre></div> <div class=clipboard-copy-txt id=cell-5>skip_keywords = [ "julia", ## [KHW] skip for now since we didn't have julia codegen rdy "builder/base", ## hiding from user "builder/terminate", ## hiding from user "ir/tree_print", ## hiding from user "ir/visitor", ## hiding from user "codegen/", ## hiding from user "builder/factory", ## hiding from user "builder_old", ## deprecated from user "task_old", ## deprecated from user "visualization", ## hiding from user "submission/capabilities", ## hiding from user "submission/quera_api_client", ]</div> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=a861b6e1"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-6> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=n>nav</span> <span class=o>=</span> <span class=n>mkdocs_gen_files</span><span class=o>.</span><span class=n>Nav</span><span class=p>()</span>
 <span class=k>for</span> <span class=n>path</span> <span class=ow>in</span> <span class=nb>sorted</span><span class=p>(</span><span class=n>Path</span><span class=p>(</span><span class=n>SRC_PATH</span><span class=p>)</span><span class=o>.</span><span class=n>rglob</span><span class=p>(</span><span class=s2>"*.py"</span><span class=p>)):</span>
     <span class=n>module_path</span> <span class=o>=</span> <span class=n>path</span><span class=o>.</span><span class=n>relative_to</span><span class=p>(</span><span class=n>SRC_PATH</span><span class=p>)</span><span class=o>.</span><span class=n>with_suffix</span><span class=p>(</span><span class=s2>""</span><span class=p>)</span>
     <span class=n>doc_path</span> <span class=o>=</span> <span class=n>path</span><span class=o>.</span><span class=n>relative_to</span><span class=p>(</span><span class=n>SRC_PATH</span><span class=p>)</span><span class=o>.</span><span class=n>with_suffix</span><span class=p>(</span><span class=s2>".md"</span><span class=p>)</span>
@@ -438,6 +438,6 @@
 
     <span class=n>mkdocs_gen_files</span><span class=o>.</span><span class=n>set_edit_path</span><span class=p>(</span><span class=n>full_doc_path</span><span class=p>,</span> <span class=s2>".."</span> <span class=o>/</span> <span class=n>path</span><span class=p>)</span>
 </pre></div> <div class=clipboard-copy-txt id=cell-6>nav = mkdocs_gen_files.Nav() for path in sorted(Path(SRC_PATH).rglob("*.py")): module_path = path.relative_to(SRC_PATH).with_suffix("") doc_path = path.relative_to(SRC_PATH).with_suffix(".md") full_doc_path = Path("reference", doc_path) iskip = False for kwrd in skip_keywords: if kwrd in str(doc_path): iskip = True break if iskip: print("[Ignore]", str(doc_path)) continue print("[&gt;]", str(doc_path)) parts = tuple(module_path.parts) if parts[-1] == "__init__": parts = parts[:-1] doc_path = doc_path.with_name("index.md") full_doc_path = full_doc_path.with_name("index.md") elif parts[-1].startswith("_"): continue nav[parts] = doc_path.as_posix() with mkdocs_gen_files.open(full_doc_path, "w") as fd: ident = ".".join(parts) fd.write(f"::: {ident}") mkdocs_gen_files.set_edit_path(full_doc_path, ".." / path)</div> </div> </div> </div> </div> <div class=jp-Cell-outputWrapper> <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> <div class="jp-OutputArea jp-Cell-outputArea"> <div class=jp-OutputArea-child> <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> <div class="jp-RenderedText jp-OutputArea-output" data-mime-type=text/plain tabindex=0> <pre>[&gt;] example_package/__init__.md
-</pre> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=5c8acb3e"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>with</span> <span class=n>mkdocs_gen_files</span><span class=o>.</span><span class=n>open</span><span class=p>(</span><span class=s2>"reference/SUMMARY.txt"</span><span class=p>,</span> <span class=s2>"w"</span><span class=p>)</span> <span class=k>as</span> <span class=n>nav_file</span><span class=p>:</span>
+</pre> </div> </div> </div> </div> </div> </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> <div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a65a82ee"> <div class=jp-Cell-inputWrapper tabindex=0> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type=inline> <div class="CodeMirror cm-s-jupyter"> <div class=zeroclipboard-container> <clipboard-copy for=cell-7> <div> <span class=notice hidden>Copied!</span> <svg aria-hidden=true class=clipboard-copy-icon data-view-component=true height=20 version=1.1 viewbox="0 0 16 16" width=20> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill=currentColor fill-rule=evenodd></path> <path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill=currentColor fill-rule=evenodd></path> </svg> </div> </clipboard-copy> </div> <div class="highlight-ipynb hl-python"><pre><span></span><span class=k>with</span> <span class=n>mkdocs_gen_files</span><span class=o>.</span><span class=n>open</span><span class=p>(</span><span class=s2>"reference/SUMMARY.txt"</span><span class=p>,</span> <span class=s2>"w"</span><span class=p>)</span> <span class=k>as</span> <span class=n>nav_file</span><span class=p>:</span>
     <span class=n>nav_file</span><span class=o>.</span><span class=n>writelines</span><span class=p>(</span><span class=n>nav</span><span class=o>.</span><span class=n>build_literate_nav</span><span class=p>())</span>
 </pre></div> <div class=clipboard-copy-txt id=cell-7>with mkdocs_gen_files.open("reference/SUMMARY.txt", "w") as nav_file: nav_file.writelines(nav.build_literate_nav())</div> </div> </div> </div> </div> </div> </div> </div> <!-- jp-Notebook --> </div> <!-- jupyter-wrapper --> </article> </div> <script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script> </div> <button type=button class="md-top md-icon" data-md-component=top hidden> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg> Back to top </button> </main> <footer class=md-footer> <div class="md-footer-meta md-typeset"> <div class="md-footer-meta__inner md-grid"> <div class=md-copyright> <div class=md-copyright__highlight> Copyright &copy; 2023 - QuEra Computing Inc. </div> Made with <a href=https://squidfunk.github.io/mkdocs-material/ target=_blank rel=noopener> Material for MkDocs </a> </div> <div class=md-social> <a href=https://github.com/QuEraComputing/bloqade-python target=_blank rel=noopener title=github.com class=md-social__link> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 496 512"><!-- Font Awesome Free 6.5.1 by @fontawesome - 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\ No newline at end of file
diff --git a/0.15.7/sitemap.xml b/0.15.7/sitemap.xml
index 705f589..80d1107 100644
--- a/0.15.7/sitemap.xml
+++ b/0.15.7/sitemap.xml
@@ -2,67 +2,67 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>https://bloqade.quera.com/0.15.7/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-1-floquet/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-1-rabi/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-1-ramsey/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-2-multi-qubit-blockaded/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-2-nonequilibrium-dynamics-blockade-radius/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-2-two-qubit-adiabatic/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-3-2d-ordered-state/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-3-time-sweep/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-4-quantum-scar-dynamics/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/examples/example-5-MIS-UDG/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/reference/example_package/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://bloqade.quera.com/0.15.7/scripts/gen_ref_nav/</loc>
-         <lastmod>2024-03-15</lastmod>
+         <lastmod>2024-03-17</lastmod>
          <changefreq>daily</changefreq>
     </url>
 </urlset>
\ No newline at end of file
diff --git a/0.15.7/sitemap.xml.gz b/0.15.7/sitemap.xml.gz
index 648f2ac..5326c0c 100644
Binary files a/0.15.7/sitemap.xml.gz and b/0.15.7/sitemap.xml.gz differ