From e00303ddc898b81e3abf7353892643ed65eaadc2 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 22 Aug 2024 18:11:21 +0200 Subject: [PATCH 01/44] Stub for ZNE Catalyst tutorial --- .../tutorial_zne_catalyst.metadata.json | 51 +++++++++++++++ demonstrations/tutorial_zne_catalyst.py | 65 +++++++++++++++++++ 2 files changed, 116 insertions(+) create mode 100644 demonstrations/tutorial_zne_catalyst.metadata.json create mode 100644 demonstrations/tutorial_zne_catalyst.py diff --git a/demonstrations/tutorial_zne_catalyst.metadata.json b/demonstrations/tutorial_zne_catalyst.metadata.json new file mode 100644 index 0000000000..165bd91866 --- /dev/null +++ b/demonstrations/tutorial_zne_catalyst.metadata.json @@ -0,0 +1,51 @@ +{ + "title": "Digital Zero-Noise Extrapolation with Catalyst", + "authors": [ + { + "id": "nate_stemen" + } + ], + "dateOfPublication": "2024-09-09T00:00:00+00:00", + "dateOfLastModification": "2024-09-09T00:00:00+00:00", + "categories": [ + "Algorithms", + "Quantum Computing", + "How-to" + ], + "tags": [], + "previewImages": [ + { + "type": "thumbnail", + "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_qrack_catalyst_integration.png" + }, + { + "type": "large_thumbnail", + "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_qrack_catalyst_integration.png" + } + ], + "seoDescription": "Digital Zero-Noise Extrapolation with Catalyst", + "doi": "", + "canonicalURL": "/qml/demos/tutorial_zne_catalyst", + "references": [ + { + "id": "DZNEpaper", + "type": "preprint", + "title": "Digital zero noise extrapolation for quantum error mitigation", + "authors": "Tudor Giurgica-Tiron, Yousef Hindy, Ryan LaRose, Andrea Mari, and William J. Zeng", + "year": "2020", + "publisher": "", + "journal": "", + "doi": "10.48550/arXiv.2005.10921", + "url": "https://arxiv.org/abs/2005.10921v2" + } + ], + "basedOnPapers": [], + "referencedByPapers": [], + "relatedContent": [ + { + "type": "demonstration", + "id": "tutorial_error_mitigation", + "weight": 1.0 + } + ] +} diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py new file mode 100644 index 0000000000..5d33561529 --- /dev/null +++ b/demonstrations/tutorial_zne_catalyst.py @@ -0,0 +1,65 @@ +r""" +Digital Zero-Noise Extrapolation with Catalyst +======================================== + +In this tutorial you will learn how to use ... and quantum just-in-time (QJIT) compilation via +`Catalyst +`__. + +What is ZNE +--------------- + +""" +import os + +import jax + +import pennylane as qml +from pennylane import numpy as np +from catalyst import qjit, mitigate_with_zne + + +############################################################################# +# We use Qrack because it implements a noise-model and it's compatible with Catalyst +qubits = 2 +NOISE_LEVEL = 0.1 +os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) +dev = qml.device("qrack.simulator", qubits, isNoisy=True) + + +@qml.qnode(dev) +def circuit(): + qml.Hadamard(wires=0) + qml.CNOT(wires=[0, 1]) + return qml.expval(qml.PauliZ(wires=0)) + + +folding_factors = jax.numpy.array([1, 2, 3]) + +@qjit +def mitigated_circuit(): + return mitigate_with_zne( + circuit, + scale_factors=folding_factors + )() + +print(circuit()) +print(mitigated_circuit()) + + +############################################################################## +# +# References +# ---------- +# +# .. [#DZNEpaper] +# +# Tudor Giurgica-Tiron, Yousef Hindy, Ryan LaRose, Andrea Mari, and William J. Zeng +# "Digital zero noise extrapolation for quantum error mitigation" +# `arXiv:2005.10921v2 <"https://arxiv.org/abs/2005.10921v2">`__, 2020. +# + +############################################################################## +# About the author +# ---------------- +# .. include:: ../_static/authors/nate_stemen.txt From 1db073f81922159a1d095969e84ea4c7e451dfad Mon Sep 17 00:00:00 2001 From: nate stemen Date: Wed, 4 Sep 2024 23:50:27 -0700 Subject: [PATCH 02/44] add description of protocol --- _static/authors/nate_stemen.png | Bin 0 -> 2457915 bytes _static/authors/nate_stemen.txt | 4 + .../tutorial_zne_catalyst.metadata.json | 3 +- demonstrations/tutorial_zne_catalyst.py | 71 +++++++++++++++--- 4 files changed, 66 insertions(+), 12 deletions(-) create mode 100644 _static/authors/nate_stemen.png create mode 100644 _static/authors/nate_stemen.txt diff --git a/_static/authors/nate_stemen.png b/_static/authors/nate_stemen.png new file mode 100644 index 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He earned a master’s degree from the University of Waterloo, where he focused on quantum circuit compilation. He is passionate about advancing effective education in quantum computing. \ No newline at end of file diff --git a/demonstrations/tutorial_zne_catalyst.metadata.json b/demonstrations/tutorial_zne_catalyst.metadata.json index 165bd91866..e080405531 100644 --- a/demonstrations/tutorial_zne_catalyst.metadata.json +++ b/demonstrations/tutorial_zne_catalyst.metadata.json @@ -2,7 +2,8 @@ "title": "Digital Zero-Noise Extrapolation with Catalyst", "authors": [ { - "id": "nate_stemen" + "id": "nate_stemen", + "affiliation": "Unitary Fund" } ], "dateOfPublication": "2024-09-09T00:00:00+00:00", diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 5d33561529..a485bd023b 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -2,14 +2,58 @@ Digital Zero-Noise Extrapolation with Catalyst ======================================== -In this tutorial you will learn how to use ... and quantum just-in-time (QJIT) compilation via -`Catalyst -`__. +In this tutorial, you will learn how to use Zero-Noise Extrapolation (ZNE) in combination with +Catalyst, a quantum programming framework. +We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum simulator, +and use extrapolation techniques to estimate the zero-noise result, all while leveraging quantum +just-in-time (QJIT) compilation via +`Catalyst `__. What is ZNE --------------- +Zero-Noise Extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum +computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum +results by running circuits at varying noise levels and extrapolating back to a hypothetical +zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail, lets first +review what happens when using the protocol in practice. + +Stage 1: Generating Noise-Scaled Circuits +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For ZNE to work, we need to generate circuits with **increased** noise. Currently, ZNE in Catalyst +supports two methods for generating noise-scaled circuits: + +1. **Global folding**: If a circuit implements a global unitary :math:`\mathcal{U}`, global folding + applies :math:`\mathcal{U}(\mathcal{U}^\dagger\mathcal{U})^n` for some integer :math:`n`, + effectively scaling the noise in the entire circuit. +2. **Local folding**: Individual gates are repeated (or folded) in contrast with the entire + circuit. + +Stage 2: Running the circuits +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Once noise-scaled circuits are created, they need to be run! These can be executed on either real +quantum hardware or a noisy quantum simulator. In this tutorial, we'll use the Qrack quantum +simulator, which implements a noise model compatible with Catalyst. + +Stage 3: Combining the results +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +After executing the noise-scaled circuits, we perform an extrapolation to estimate the zero-noise +limit---the result we would expect in a noise-free scenario. Catalyst provides two methods for +perfoming this extrapolation: + +1. **Polynomial extrapolation**, and +2. **Exponential extrapolation**. + + +Using ZNE in Catalyst +--------------------- + +Let's see what this workflow looks like using Catalyst. """ + import os import jax @@ -34,29 +78,34 @@ def circuit(): return qml.expval(qml.PauliZ(wires=0)) -folding_factors = jax.numpy.array([1, 2, 3]) +folding_factors = jax.numpy.array([1, 3, 5]) + @qjit def mitigated_circuit(): - return mitigate_with_zne( - circuit, - scale_factors=folding_factors - )() + return mitigate_with_zne(circuit, scale_factors=folding_factors)() + print(circuit()) print(mitigated_circuit()) +############################################################################## +# + ############################################################################## -# +# # References # ---------- # -# .. [#DZNEpaper] +# .. [#temme2017zne] K. Temme, S. Bravyi, J. M. Gambetta +# `"Error Mitigation for Short-Depth Quantum Circuits" `_, +# Phys. Rev. Lett. 119, 180509 (2017). # +# .. [#DZNEpaper] # Tudor Giurgica-Tiron, Yousef Hindy, Ryan LaRose, Andrea Mari, and William J. Zeng # "Digital zero noise extrapolation for quantum error mitigation" -# `arXiv:2005.10921v2 <"https://arxiv.org/abs/2005.10921v2">`__, 2020. +# `arXiv:2005.10921v2 `__, 2020. # ############################################################################## From 9038c943fe4566adfe54cb4e2df4f3c4a66f10d6 Mon Sep 17 00:00:00 2001 From: nate stemen Date: Thu, 5 Sep 2024 20:25:34 -0700 Subject: [PATCH 03/44] flush out initial workflow; lead into comparison --- demonstrations/tutorial_zne_catalyst.py | 82 +++++++++++++++++++------ 1 file changed, 64 insertions(+), 18 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index a485bd023b..0714b1def8 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -1,16 +1,15 @@ r""" Digital Zero-Noise Extrapolation with Catalyst -======================================== +============================================== In this tutorial, you will learn how to use Zero-Noise Extrapolation (ZNE) in combination with -Catalyst, a quantum programming framework. -We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum simulator, -and use extrapolation techniques to estimate the zero-noise result, all while leveraging quantum -just-in-time (QJIT) compilation via -`Catalyst `__. +Catalyst. We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum +simulator, and use extrapolation techniques to estimate the zero-noise result, all while +leveraging just-in-time (JIT) compilation through +`Catalyst `_. What is ZNE ---------------- +----------- Zero-Noise Extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum @@ -24,18 +23,19 @@ For ZNE to work, we need to generate circuits with **increased** noise. Currently, ZNE in Catalyst supports two methods for generating noise-scaled circuits: -1. **Global folding**: If a circuit implements a global unitary :math:`\mathcal{U}`, global folding - applies :math:`\mathcal{U}(\mathcal{U}^\dagger\mathcal{U})^n` for some integer :math:`n`, - effectively scaling the noise in the entire circuit. +1. **Global folding**: If a circuit implements a global unitary :math:`U`, global folding applies + :math:`U(U^\dagger U)^n` for some integer :math:`n`, + effectively scaling the noise in the entire circuit. 2. **Local folding**: Individual gates are repeated (or folded) in contrast with the entire - circuit. + circuit. Stage 2: Running the circuits ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Once noise-scaled circuits are created, they need to be run! These can be executed on either real -quantum hardware or a noisy quantum simulator. In this tutorial, we'll use the Qrack quantum -simulator, which implements a noise model compatible with Catalyst. +quantum hardware or a noisy quantum simulator. In this tutorial, we'll use the +`Qrack quantum simulator `_, which implements a noise model +compatible with Catalyst. Stage 3: Combining the results ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -52,6 +52,15 @@ --------------------- Let's see what this workflow looks like using Catalyst. +To get things started we'll need two ingredients: + +1. an example quantum circuit to run +2. a way to execute circuits + +The circuits we use here will be ..., and the method of execution will be a noisy simulation +available through Qrack. The underlying noise model of this simulation is that of depolarizing +noise. + """ import os @@ -63,8 +72,6 @@ from catalyst import qjit, mitigate_with_zne -############################################################################# -# We use Qrack because it implements a noise-model and it's compatible with Catalyst qubits = 2 NOISE_LEVEL = 0.1 os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) @@ -78,19 +85,58 @@ def circuit(): return qml.expval(qml.PauliZ(wires=0)) -folding_factors = jax.numpy.array([1, 3, 5]) +############################################################################## +# With a circuit and simulator defined, we can begin to define some of the necessary parameters +# for ZNE. In particular we will need to specify +# +# 1. The noise scaling factors (i.e. how much to increase the depth of the circuit) +# 2. The _method_ for scaling this noise up (in Catalyst there are two options: `global` and +# `local`) +# 3. The extrapolation technique to use to estimate the ideal value (available in Catalyst are +# polynomial, and exponential extrapolation). +# +# We'll define the scale factors as a `jax.numpy.array` where the scale factors must be odd +# integers. + +scale_factors = jax.numpy.array([1, 3, 5]) + +############################################################################## +# Next we'll choose a method for scaling the noise. This needs to be defined as a python string. + +folding_method = "local" + +############################################################################## +# Finally, we'll choose the extrapolation technique. Both exponential and polynoamial extrapolation +# is available in the `pennylane.transforms` module. Both of these functions can be passed directly +# into `mitigate_with_zne`! + +from pennylane.transforms import exponential_extrapolate, poly_extrapolate + +extrapolation_method = exponential_extrapolate + +############################################################################## +# We're now ready to run our example using ZNE with Catalyst! Putting these all together we're able +# to define a very simple `QNode`! @qjit def mitigated_circuit(): - return mitigate_with_zne(circuit, scale_factors=folding_factors)() + return mitigate_with_zne( + circuit, + scale_factors=scale_factors, + extrapolate=extrapolation_method, + folding=folding_method, + )() print(circuit()) print(mitigated_circuit()) ############################################################################## -# +# But there's still a big unanswered question! _If I can do this all in PennyLane, what is Catalyst +# offering here?_ That's a **great** question! In order to explore the difference we'll need to +# explore what happens when `catalyst.qjit` is, and is not, used! +# ... ############################################################################## From faa3c18a47afe62a06f691868cf69a5465c702f5 Mon Sep 17 00:00:00 2001 From: nate stemen Date: Sat, 7 Sep 2024 08:59:42 +0300 Subject: [PATCH 04/44] Suggestions from @vprusso Co-Authored-By: Vincent Russo --- demonstrations/tutorial_zne_catalyst.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 0714b1def8..b5925b417a 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -87,7 +87,7 @@ def circuit(): ############################################################################## # With a circuit and simulator defined, we can begin to define some of the necessary parameters -# for ZNE. In particular we will need to specify +# In particular we will need to specify: # # 1. The noise scaling factors (i.e. how much to increase the depth of the circuit) # 2. The _method_ for scaling this noise up (in Catalyst there are two options: `global` and @@ -101,7 +101,7 @@ def circuit(): scale_factors = jax.numpy.array([1, 3, 5]) ############################################################################## -# Next we'll choose a method for scaling the noise. This needs to be defined as a python string. +# Next, we'll choose a method to scale the noise. This needs to be defined as a Python string. folding_method = "local" From 771d508619733bfb0439ce69c1be1b2cbb35f20b Mon Sep 17 00:00:00 2001 From: cmt Date: Wed, 28 Aug 2024 12:52:38 +0100 Subject: [PATCH 05/44] Add tutorial (#1075) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ------------------------------------------------------------------------------------------------------------ **Title:** How to implement VQD with PennyLane **Summary:** **Relevant references:** **Possible Drawbacks:** **Related GitHub Issues:** ---- If you are writing a demonstration, please answer these questions to facilitate the marketing process. * GOALS — Finding first excitation energy is necessary to model reaction * AUDIENCE — Chemistry researchers, beginners in quantum computing. * KEYWORDS — Quantum Chemistry * Which of the following types of documentation is most similar to your file? (more details [here](https://www.notion.so/xanaduai/Different-kinds-of-documentation-69200645fe59442991c71f9e7d8a77f8)) - [x] Tutorial - [x] Demo - [x] How-to [sc-64060] --------- Co-authored-by: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Co-authored-by: soranjh <40344468+soranjh@users.noreply.github.com> Co-authored-by: Ivana Kurečić Co-authored-by: Ivana Kurečić --- _static/authors/minh_chau.jpg | Bin 0 -> 25108 bytes _static/authors/minh_chau.txt | 4 + .../thumbnail_large_how_to_vqd_pennylane.png | Bin 0 -> 150899 bytes .../OGthumbnail_how_to_vqd_pennylane.png | Bin 0 -> 146578 bytes .../thumbnail_how_to_vqd_pennylane.png | Bin 0 -> 34711 bytes demonstrations/tutorial_vqe_vqd.metadata.json | 58 +++++ demonstrations/tutorial_vqe_vqd.py | 220 ++++++++++++++++++ 7 files changed, 282 insertions(+) create mode 100644 _static/authors/minh_chau.jpg create mode 100644 _static/authors/minh_chau.txt create mode 100644 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"How-to" + ], + "tags": [], + "previewImages": [ + { + "type": "thumbnail", + "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_how_to_vqd_pennylane.png" + }, + { + "type": "large_thumbnail", + "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_how_to_vqd_pennylane.png" + } + ], + "seoDescription": "Learn how to use variational quantum deflation (VQD) to find the first excited state energy of the hydrogen molecule.", + "doi": "", + "canonicalURL": "/qml/demos/tutorial_vqd", + "references": [ + { + + "id": "Higgott2019variationalquantum", + "type": "article", + "title": "Variational Quantum Computation of Excited States", + "authors": "Higgott, Oscar and Wang, Daochen and Brierley, Stephen", + "year": "2019", + "journal": "Quantum", + "url": "https://doi.org/10.22331/q-2019-07-01-156" + } + ], + "basedOnPapers": [], + "referencedByPapers": [], + "relatedContent": [ + { + "type": "demonstration", + "id": "tutorial_vqe", + "weight": 1.0 + }, + { + "type": "demonstration", + "id": "tutorial_givens_rotations", + "weight": 1.0 + } + ], + "hardware": [] +} diff --git a/demonstrations/tutorial_vqe_vqd.py b/demonstrations/tutorial_vqe_vqd.py new file mode 100644 index 0000000000..7b17ba830c --- /dev/null +++ b/demonstrations/tutorial_vqe_vqd.py @@ -0,0 +1,220 @@ +r"""How to implement VQD with PennyLane +=============================================================== + +Finding the eigenvalues of a Hamiltonian is a key task in quantum computing. Algorithms such as the variational quantum eigensolver (VQE) are used to find the smallest +eigenvalue, but sometimes we are interested in other eigenvalues. Here we will show you how to implement the variational +quantum deflation (VQD) algorithm in PennyLane +and find the first excited state energy of the `hydrogen molecule `__. To benefit the most from this tutorial, we recommend +a familiarization with the `variational quantum eigensolver (VQE) algorithm `__ first. + +.. figure:: ../_static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_how_to_vqd_pennylane.png + :align: center + :width: 70% + :target: javascript:void(0) + +""" + +###################################################################### +# +# Variational quantum deflation +# ------------------------------ +# +# The VQD algorithm [#Vqd]_ is a method used to find the excited states of a quantum system. +# It is related to the `VQE algorithm `__, which is often used to find the ground state energy of a quantum system. +# The main idea of the VQE algorithm is to define a quantum state ansatz that depends on adjustable parameters :math:`\theta` and minimize the energy of the system, computed as: +# +# .. math:: C_0(\theta) = \left\langle\Psi_0 (\theta)|\hat H |\Psi_0 (\theta) \right\rangle, +# +# where :math:`\Psi_0(\theta)` is the ansatz. However, this is not enough if we are interested in states other than the ground state. +# We must find a function whose minimum is no longer the ground state energy but gives the next excited state. +# This is possible by just adding a penalty term to the above function, which accounts for the orthogonality of the states: +# +# .. math:: C_1(\theta) = \left\langle\Psi(\theta)|\hat H |\Psi (\theta) \right\rangle + \beta | \left\langle \Psi (\theta)| \Psi_0 \right\rangle|^2, +# +# where :math:`\beta` is a hyperparameter that controls the penalty term and :math:`| \Psi \rangle` is the excited state. +# Note that :math:`\beta` should be larger than the energy gap between the ground and excited states. +# This function can now be minimized to give the first excited state energy. +# Similarly, we could iteratively calculate the :math:`k`-th excited states by adding the corresponding penalty term to the previous :math:`k - 1` excited state. +# +# As easy as that! Let's see how we can run this using PennyLane. +# +# +# Finding the ground state +# ------------------------------------------- +# +# To implement the VQD algorithm, we first need to know the ground state of our system, and it is a breeze to use the data from `PennyLane Datasets `__ to obtain the Hamiltonian and the ground state +# of the hydrogen molecule: +# + +import pennylane as qml +from pennylane import numpy as np + +# Load the dataset +h2 = qml.data.load("qchem", molname="H2", bondlength=0.742, basis="STO-3G")[0] + +# Extract the Hamiltonian +H, n_qubits = h2.hamiltonian, len(h2.hamiltonian.wires) + + +# Obtain the ground state from the operations given by the dataset +def generate_ground_state(wires): + qml.BasisState(h2.hf_state, wires=wires) + + for op in h2.vqe_gates: # use the gates data from the dataset + op = qml.map_wires(op, {op.wires[i]: wires[i] for i in range(len(wires))}) + qml.apply(op) + +###################################################################### +# The ``generate_ground_state`` function prepares the ground state of the molecule using the data obtained from the dataset. +# Let's use it to check the energy of that state: +# + +dev = qml.device("default.qubit") + +@qml.qnode(dev) +def circuit(): + generate_ground_state(range(n_qubits)) + return qml.expval(H) + +print(f"Ground state energy: {circuit()}") + +###################################################################### +# We now use the ground state to find the first excited state. +# +# Finding the excited state +# ---------------------------- +# +# To obtain the excited state we must define our ansatz that generates the state :math:`|\Psi(\theta)\rangle`. +# +# We use an ansatz constructed with :doc:`Givens rotations `, and we define the circuit for finding the excited state. +# + +from functools import partial + +# This line is added to better visualise the circuit +@partial(qml.devices.preprocess.decompose, stopping_condition = lambda obj:False, max_expansion=1) + +def ansatz(theta, wires): + singles, doubles = qml.qchem.excitations(2, n_qubits) + singles = [[wires[i] for i in single] for single in singles] + doubles = [[wires[i] for i in double] for double in doubles] + qml.AllSinglesDoubles(theta, wires, np.array([1,1,0,0]), singles, doubles) + +theta = np.random.rand(3) # 3 parameters for the ansatz +print(qml.draw(ansatz, decimals = 2)(theta, range(4))) + +###################################################################### +# The ``ansatz`` function is the one that generates the state :math:`|\Psi(\theta)\rangle`. +# The next step is to calculate the overlap between our generated state and the ground state, using a technique +# known as `swap test `__. + + +@qml.qnode(dev) +def swap_test(params): + generate_ground_state(range(1, n_qubits + 1)) + ansatz(params, range(n_qubits + 1, 2 * n_qubits + 1)) + + qml.Barrier() # added to better visualise the circuit + qml.Hadamard(wires=0) + for i in range(n_qubits): + qml.CSWAP(wires=[0, 1 + i + n_qubits, 1 + i]) + qml.Hadamard(wires=0) + return qml.expval(qml.Z(0)) + +print(qml.draw(swap_test)(theta)) +print(f"\nOverlap between the ground state and the ansatz: {swap_test(theta)}") + +###################################################################### +# The ``swap_test`` function returns the overlap between the generated state and the ground state. +# +# With this we have all the ingredients to define the loss function that we want to minimize: +# + +@qml.qnode(dev) +def expected_value(theta): + ansatz(theta, range(n_qubits)) + return qml.expval(H) + +def loss_f(theta, beta): + return expected_value(theta) + beta * swap_test(theta) + +###################################################################### +# The ``loss_f`` function returns the value of the cost function. +# The next step is to optimize the parameters of the ansatz to minimize the cost function. + +import jax +import optax + +jax.config.update("jax_platform_name", "cpu") +jax.config.update("jax_enable_x64", True) +print() + +theta = jax.numpy.array([0.1, 0.2, 0.3]) +beta = 2 + +# Store the values of the cost function +energy = [loss_f(theta, beta)] + +conv_tol = 1e-6 +max_iterations = 100 + +opt = optax.sgd(learning_rate=0.4) + +# Store the values of the circuit parameter +angle = [theta] + +opt_state = opt.init(theta) + +for n in range(max_iterations): + gradient = jax.grad(loss_f)(theta, beta) + updates, opt_state = opt.update(gradient, opt_state) + theta = optax.apply_updates(theta, updates) + angle.append(theta) + energy.append(loss_f(theta, beta)) + + conv = jax.numpy.abs(energy[-1] - energy[-2]) + + if n % 5 == 0: + print(f"Step = {n}, Energy = {energy[-1]:.8f} Ha") + + if conv <= conv_tol: + break + +print(f"\nEstimated energy: {energy[-1].real:.8f}") + +###################################################################### +# Great! We have found a new energy value, but is this the right one? +# One way to check is to access the eigenvalues of the Hamiltonian directly: + +print(np.sort(np.linalg.eigvals(H.matrix()))) + +###################################################################### +# We have indeed found an eigenvalue of the Hamiltonian. It may seem that we have skipped the value :math:`-0.5389`, +# however the eigenvector corresponding to this eigenvalue belongs to a different particle number sector. +# The correct energy value for the first excited state of hydrogen is :math:`-0.53320939`, consistent with what we obtained with VQD! +# We have successfully found the first excited state! +# +# Conclusion +# ---------- +# +# VQD is a variational method that can be used for calculating low-level excited state energies of quantum systems. Leveraging the +# orthogonality of the eigenstates, it adds a regularization penalty to the cost function to allow the search for +# the next excited state from the ground state discovered by VQE. +# +# In this tutorial we delved into the capabilities of variational quantum deflation (VQD) using PennyLane to compute +# the excited states of a hydrogen molecule. +# This illustrated how advanced quantum algorithms can extend beyond basic applications, +# offering deeper insights into quantum systems. We invite you to continue exploring these techniques and find more interesting use cases. +# +# References +# ---------- +# +# .. [#Vqd] +# +# Higgott, Oscar and Wang, Daochen and Brierley, Stephen +# "Variational Quantum Computation of Excited States" +# `Quantum 3, 156 (2019) `__. +# +# About the authors +# ----------------- +# From 25e95e1528d707ac63c2f4cabc3a8eac82cfea2e Mon Sep 17 00:00:00 2001 From: Ashish Kanwar Singh <104938869+ashishks0522@users.noreply.github.com> Date: Wed, 28 Aug 2024 11:01:59 -0400 Subject: [PATCH 06/44] fix-tutorial-vqe-vqd-metadata (#1200) ## Changes - FIx metadata URL --- demonstrations/tutorial_vqe_vqd.metadata.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/demonstrations/tutorial_vqe_vqd.metadata.json b/demonstrations/tutorial_vqe_vqd.metadata.json index ee9c963365..2fd6e84b00 100644 --- a/demonstrations/tutorial_vqe_vqd.metadata.json +++ b/demonstrations/tutorial_vqe_vqd.metadata.json @@ -9,7 +9,7 @@ } ], "dateOfPublication": "2024-08-26T00:00:00+00:00", - "dateOfLastModification": "2024-08-26T00:00:00+00:00", + "dateOfLastModification": "2024-08-28T00:00:00+00:00", "categories": [ "Quantum Chemistry", "How-to" @@ -27,7 +27,7 @@ ], "seoDescription": "Learn how to use variational quantum deflation (VQD) to find the first excited state energy of the hydrogen molecule.", "doi": "", - "canonicalURL": "/qml/demos/tutorial_vqd", + "canonicalURL": "/qml/demos/tutorial_vqe_vqd", "references": [ { From 1a2ccf1d2e4c80e9da50f7cf79f5105f56d1b78f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ivana=20Kure=C4=8Di=C4=87?= Date: Wed, 28 Aug 2024 21:41:23 +0100 Subject: [PATCH 07/44] Fixing incorrect title of Demo in metadata file. (#1197) See title change. --- .../tutorial_learning_dynamics_incoherently.metadata.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demonstrations/tutorial_learning_dynamics_incoherently.metadata.json b/demonstrations/tutorial_learning_dynamics_incoherently.metadata.json index 2ebd647ce5..6d720568e3 100644 --- a/demonstrations/tutorial_learning_dynamics_incoherently.metadata.json +++ b/demonstrations/tutorial_learning_dynamics_incoherently.metadata.json @@ -1,5 +1,5 @@ { - "title": "Learning dynamics incoherently", + "title": "Learning dynamics incoherently: Variational learning using classical shadows", "authors": [ { "username": "Diego" From 7d91d080302a7e5c81dd10e8352db99996f035c5 Mon Sep 17 00:00:00 2001 From: Ashish Kanwar Singh <104938869+ashishks0522@users.noreply.github.com> Date: Thu, 29 Aug 2024 16:53:47 -0400 Subject: [PATCH 08/44] sc-70154-update-qml-readme-to-highlight-image-standards (#1184) ## Ticket - [sc-70154-update-qml-readme-to-highlight-image-standards](https://app.shortcut.com/xanaduai/story/70154/update-qml-readme-to-highlight-image-standards) ## Changes - Add instructions to `README.md` about image standards to be followed while writing demos this QML repository. - Requirements sources from this [doc](https://www.notion.so/xanaduai/Image-standards-for-web-projects-264c09e45a8941a597918b878a2f1a5e) - Since QML is public & the notion doc is private (accessible by Xanadu only) added the relevant standards in text form in the `README.md` ## Preview - https://github.com/PennyLaneAI/qml/blob/2e9da1bb2bef3cd8a2eb504459f686ab20b9ec0c/README.md --- README.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/README.md b/README.md index 61a8415c64..dca364faed 100644 --- a/README.md +++ b/README.md @@ -66,8 +66,23 @@ quantum computing paper/result. where `` is a sub-directory with the name of your demo. +

    + Follow these standards when adding images + + ### File Size + * Always aim to keep the image file size in kilobytes (KB’s) + * Always compress the image to the best possible size where quality is acceptable. + + ### Formats + * Use `.png` for everything (decorative images, descriptive images, logos etc) + * Use `.gif` for animated images + + ### Dimensions + * To maintain quality and performance, every image should be twice (2X) its visible dimension size on the web page, and at a minimum of `150 ppi/dpi` (preferably `300 ppi/dpi`). +

    - Add and select an author photo from the `_static/authors` folder. The image name should be as `_.`. If this is a new author and their image is not a headshot, store the original image as `__original.` and create a cropped headshot with the aforementioned name. + - In the same folder create a `.txt` file where to include the bio following this structure: ```txt From 645e7631ca907535fef431244b1cbb39125d8810 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Sat, 7 Sep 2024 18:25:31 +0200 Subject: [PATCH 09/44] Landscape of qem techniques in pennylane --- demonstrations/tutorial_zne_catalyst.py | 48 +++++++++++++++++++++---- 1 file changed, 42 insertions(+), 6 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index b5925b417a..e77b954fb6 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -14,14 +14,14 @@ Zero-Noise Extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum results by running circuits at varying noise levels and extrapolating back to a hypothetical -zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail, lets first +zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail, let's first review what happens when using the protocol in practice. Stage 1: Generating Noise-Scaled Circuits ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -For ZNE to work, we need to generate circuits with **increased** noise. Currently, ZNE in Catalyst -supports two methods for generating noise-scaled circuits: +In its digital version [#DZNEpaper]_, ZNE works by generating circuits with **increased** noise. +Currently, ZNE in Catalyst supports two methods for generating noise-scaled circuits: 1. **Global folding**: If a circuit implements a global unitary :math:`U`, global folding applies :math:`U(U^\dagger U)^n` for some integer :math:`n`, @@ -34,8 +34,9 @@ Once noise-scaled circuits are created, they need to be run! These can be executed on either real quantum hardware or a noisy quantum simulator. In this tutorial, we'll use the -`Qrack quantum simulator `_, which implements a noise model -compatible with Catalyst. +`Qrack quantum simulator `_, which is both compatible with Catalyst, +and implements a noise model. For more about the integration of Qrack and Catalyst, see +the demo `QJIT compilation with Qrack and Catalyst `_. Stage 3: Combining the results ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -47,6 +48,16 @@ 1. **Polynomial extrapolation**, and 2. **Exponential extrapolation**. +Using ZNE with Pennylane +------------------------ +The demo `Error mitigation with Mitiq and PennyLane `_ +shows how ZNE, along with other error mitigation techniques, can be carried out in Pennylane by using Mitiq, +a Python library developed by Unitary Fund. + +ZNE in particular is also offered out of the box in Pennylane as a *differentiable* error mitigation technique, +for usage in combination with variational workflows. More on this in the tutorial +`Differentiating quantum error mitigation transforms `_. + Using ZNE in Catalyst --------------------- @@ -103,7 +114,7 @@ def circuit(): ############################################################################## # Next, we'll choose a method to scale the noise. This needs to be defined as a Python string. -folding_method = "local" +folding_method = "global" ############################################################################## # Finally, we'll choose the extrapolation technique. Both exponential and polynoamial extrapolation @@ -139,6 +150,31 @@ def mitigated_circuit(): # ... +############################################################################## +# Here is a recap of the landscape of QEM techniques available in Pennylane. +# +# .. list-table:: +# :widths: 30 35 20 15 +# :header-rows: 1 +# +# * - **Framework** +# - **Techniques** +# - **Differentiable** +# - **JIT** +# * - Pennylane + Mitiq +# - ZNE, PEC, CDR, DDD, REM +# - +# - +# * - Pennylane transforms +# - ZNE +# - ✅ +# - +# * - Catalyst +# - ZNE +# - ✅ +# - ✅ + + ############################################################################## # # References From af5773b5fc62c7d379217bcd66e99f683e64c4f9 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Mon, 9 Sep 2024 11:19:31 +0200 Subject: [PATCH 10/44] example with mirror circuits --- demonstrations/tutorial_zne_catalyst.py | 66 +++++++++++++++---------- 1 file changed, 40 insertions(+), 26 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index e77b954fb6..28718dcf9b 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -62,15 +62,12 @@ Using ZNE in Catalyst --------------------- -Let's see what this workflow looks like using Catalyst. -To get things started we'll need two ingredients: +ZNE is also available for just-in-time (JIT) compilation of PennyLane programs, +starting from Catalyst v0.8.1. +Let's see how an error mitigation routine can be integrated in a Catalyst workflow. -1. an example quantum circuit to run -2. a way to execute circuits - -The circuits we use here will be ..., and the method of execution will be a noisy simulation -available through Qrack. The underlying noise model of this simulation is that of depolarizing -noise. +We start with defining a 4-qubit circuit, ... +and we measure the expectation value :math:`\langle Z\rangle` on the state of the first qubit. """ @@ -82,18 +79,36 @@ from pennylane import numpy as np from catalyst import qjit, mitigate_with_zne +n_wires = 4 -qubits = 2 -NOISE_LEVEL = 0.1 -os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) -dev = qml.device("qrack.simulator", qubits, isNoisy=True) +np.random.seed(42) +n_layers = 1 +template = qml.SimplifiedTwoDesign +weights_shape = template.shape(n_layers, n_wires) +w1, w2 = [2 * np.pi * np.random.random(s) for s in weights_shape] -@qml.qnode(dev) -def circuit(): - qml.Hadamard(wires=0) - qml.CNOT(wires=[0, 1]) - return qml.expval(qml.PauliZ(wires=0)) + +def circuit(w1, w2): + template(w1, w2, wires=range(n_wires)) + qml.adjoint(template)(w1, w2, wires=range(n_wires)) + return qml.expval(qml.PauliZ(0)) + +############################################################################## +# We execute the circuit on the Qrack simulator, first without noise, and then in a nosy scenario. + +noiseless_device = qml.device("qrack.simulator", n_wires, isNoisy=False) + +ideal_value = qml.QNode(circuit, device=noiseless_device)(w1, w2) +print(f"Ideal value: {ideal_value}") + +NOISE_LEVEL = 0.2 +os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) +noisy_device = qml.device("qrack.simulator", n_wires, isNoisy=True) + +noisy_qnode = qml.QNode(circuit, device=noisy_device) +noisy_value = noisy_qnode(w1, w2) +print(f"Error without mitigation: {abs((ideal_value - noisy_value) / ideal_value):.3f}") ############################################################################## @@ -106,10 +121,10 @@ def circuit(): # 3. The extrapolation technique to use to estimate the ideal value (available in Catalyst are # polynomial, and exponential extrapolation). # -# We'll define the scale factors as a `jax.numpy.array` where the scale factors must be odd -# integers. +# We'll define the scale factors as a `jax.numpy.array` where the scale factors represents +# the number of time the circuit is folded. -scale_factors = jax.numpy.array([1, 3, 5]) +scale_factors = jax.numpy.array([1, 2, 3]) ############################################################################## # Next, we'll choose a method to scale the noise. This needs to be defined as a Python string. @@ -131,17 +146,16 @@ def circuit(): @qjit -def mitigated_circuit(): +def mitigated_circuit(w1, w2): return mitigate_with_zne( - circuit, + noisy_qnode, scale_factors=scale_factors, extrapolate=extrapolation_method, folding=folding_method, - )() - + )(w1, w2) -print(circuit()) -print(mitigated_circuit()) +zne_value = mitigated_circuit(w1, w2) +print(f"Error with mitigation: {abs((ideal_value - zne_value) / ideal_value):.3f}") ############################################################################## # But there's still a big unanswered question! _If I can do this all in PennyLane, what is Catalyst From b6aa3376fabfcb28a62f88ce805dda4591879db3 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Mon, 9 Sep 2024 17:39:32 +0200 Subject: [PATCH 11/44] zne in non-catalyst pennylane --- demonstrations/tutorial_zne_catalyst.py | 25 +++++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 28718dcf9b..b473d99aa7 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -108,7 +108,7 @@ def circuit(w1, w2): noisy_qnode = qml.QNode(circuit, device=noisy_device) noisy_value = noisy_qnode(w1, w2) -print(f"Error without mitigation: {abs((ideal_value - noisy_value) / ideal_value):.3f}") +print(f"Error without mitigation: {abs(ideal_value - noisy_value):.3f}") ############################################################################## @@ -136,9 +136,9 @@ def circuit(w1, w2): # is available in the `pennylane.transforms` module. Both of these functions can be passed directly # into `mitigate_with_zne`! -from pennylane.transforms import exponential_extrapolate, poly_extrapolate +from pennylane.transforms import exponential_extrapolate, richardson_extrapolate -extrapolation_method = exponential_extrapolate +extrapolation_method = richardson_extrapolate ############################################################################## # We're now ready to run our example using ZNE with Catalyst! Putting these all together we're able @@ -146,7 +146,7 @@ def circuit(w1, w2): @qjit -def mitigated_circuit(w1, w2): +def mitigated_circuit_qjit(w1, w2): return mitigate_with_zne( noisy_qnode, scale_factors=scale_factors, @@ -154,8 +154,8 @@ def mitigated_circuit(w1, w2): folding=folding_method, )(w1, w2) -zne_value = mitigated_circuit(w1, w2) -print(f"Error with mitigation: {abs((ideal_value - zne_value) / ideal_value):.3f}") +zne_value = mitigated_circuit_qjit(w1, w2) +print(f"Error with mitigation: {abs(ideal_value - zne_value):.3f}") ############################################################################## # But there's still a big unanswered question! _If I can do this all in PennyLane, what is Catalyst @@ -163,6 +163,19 @@ def mitigated_circuit(w1, w2): # explore what happens when `catalyst.qjit` is, and is not, used! # ... +# ZNE in non-catalyst pennylane +def mitigated_circuit(w1, w2): + return qml.transforms.mitigate_with_zne( + noisy_qnode, + scale_factors=scale_factors, + extrapolate=extrapolation_method, + folding=qml.transforms.fold_global, + )(w1, w2) + +zne_value = mitigated_circuit(w1, w2) +print(f"Error with mitigation: {abs(ideal_value - zne_value):.3f}") + + ############################################################################## # Here is a recap of the landscape of QEM techniques available in Pennylane. From 6fd8aa4b8a9ffdf8491361ce3388a397bf219dcf Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Wed, 18 Sep 2024 19:33:37 +0200 Subject: [PATCH 12/44] measure running time --- demonstrations/tutorial_zne_catalyst.py | 72 ++++++++++++++++++------- 1 file changed, 52 insertions(+), 20 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index b473d99aa7..12b4e20970 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -64,26 +64,32 @@ ZNE is also available for just-in-time (JIT) compilation of PennyLane programs, starting from Catalyst v0.8.1. -Let's see how an error mitigation routine can be integrated in a Catalyst workflow. +In this tutorial we see how an error mitigation routine can be integrated in a Catalyst workflow. -We start with defining a 4-qubit circuit, ... -and we measure the expectation value :math:`\langle Z\rangle` on the state of the first qubit. +At the end of the tutorial, we will compare time for the execution of ZNE routines in +Pennylane vs. Catalyst. +Mirror circuit +-------------- + +First we build a mirror-circuit starting off a unitary 2-design. +This is a typical construction for a randomized benchmarking circuit, which is used in many tasks +in quantum computing. Given such circuit, we measure the expectation value :math:`\langle Z\rangle` +on the state of the first qubit. """ import os - -import jax +import timeit import pennylane as qml from pennylane import numpy as np from catalyst import qjit, mitigate_with_zne -n_wires = 4 +n_wires = 5 np.random.seed(42) -n_layers = 1 +n_layers = 10 template = qml.SimplifiedTwoDesign weights_shape = template.shape(n_layers, n_wires) w1, w2 = [2 * np.pi * np.random.random(s) for s in weights_shape] @@ -95,21 +101,25 @@ def circuit(w1, w2): return qml.expval(qml.PauliZ(0)) ############################################################################## -# We execute the circuit on the Qrack simulator, first without noise, and then in a nosy scenario. +# We execute the circuit on the Qrack simulator, first without noise, +# and then in a noisy scenario. noiseless_device = qml.device("qrack.simulator", n_wires, isNoisy=False) ideal_value = qml.QNode(circuit, device=noiseless_device)(w1, w2) print(f"Ideal value: {ideal_value}") -NOISE_LEVEL = 0.2 +############################################################################## +# As expected, in the noiseless scenario, the first qubit is in the :math:|0\rangle` +# state, so the expecation value of the Pauli-Z measurement is equal to 1. + +NOISE_LEVEL = 0.01 os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) noisy_device = qml.device("qrack.simulator", n_wires, isNoisy=True) noisy_qnode = qml.QNode(circuit, device=noisy_device) noisy_value = noisy_qnode(w1, w2) -print(f"Error without mitigation: {abs(ideal_value - noisy_value):.3f}") - +print(f"Error without mitigation: {abs(ideal_value - noisy_value):.8f}") ############################################################################## # With a circuit and simulator defined, we can begin to define some of the necessary parameters @@ -124,7 +134,7 @@ def circuit(w1, w2): # We'll define the scale factors as a `jax.numpy.array` where the scale factors represents # the number of time the circuit is folded. -scale_factors = jax.numpy.array([1, 2, 3]) +scale_factors = [1, 3, 5] ############################################################################## # Next, we'll choose a method to scale the noise. This needs to be defined as a Python string. @@ -136,7 +146,7 @@ def circuit(w1, w2): # is available in the `pennylane.transforms` module. Both of these functions can be passed directly # into `mitigate_with_zne`! -from pennylane.transforms import exponential_extrapolate, richardson_extrapolate +from pennylane.transforms import richardson_extrapolate extrapolation_method = richardson_extrapolate @@ -154,16 +164,17 @@ def mitigated_circuit_qjit(w1, w2): folding=folding_method, )(w1, w2) + zne_value = mitigated_circuit_qjit(w1, w2) -print(f"Error with mitigation: {abs(ideal_value - zne_value):.3f}") + +print(f"Error with ZNE in Catalyst: {abs(ideal_value - zne_value):.3f}") + ############################################################################## -# But there's still a big unanswered question! _If I can do this all in PennyLane, what is Catalyst -# offering here?_ That's a **great** question! In order to explore the difference we'll need to -# explore what happens when `catalyst.qjit` is, and is not, used! -# ... +# ZNE in Pennylane +# -------------------------------------------------------- +# Notice -# ZNE in non-catalyst pennylane def mitigated_circuit(w1, w2): return qml.transforms.mitigate_with_zne( noisy_qnode, @@ -173,7 +184,28 @@ def mitigated_circuit(w1, w2): )(w1, w2) zne_value = mitigated_circuit(w1, w2) -print(f"Error with mitigation: {abs(ideal_value - zne_value):.3f}") + +print(f"Error with ZNE in Pennylane: {abs(ideal_value - zne_value):.3f}") + +############################################################################## +# We use the timeit module in Python to measure execution time of a mitigated circuit +# ... + +repeat = 5 # number of timing runs +number = 5 # number of loops executed in each timing run + +times = timeit.repeat( + "mitigated_circuit(w1, w2)", + globals=globals(), number=number, repeat=repeat) + +print(f"Pennylane running time (best of {repeat}): {min(times):.3f}") + +times = timeit.repeat( + "mitigated_circuit_qjit(w1, w2)", + globals=globals(), number=number, repeat=repeat) + +print(f"Catalyst running time (best of {repeat}): {min(times):.3f}") + From 231c26eb5d3dc8735dab100cca8a5ce9df3aed14 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 19 Sep 2024 16:40:59 +0200 Subject: [PATCH 13/44] better explanations --- demonstrations/tutorial_zne_catalyst.py | 135 ++++++++++++++---------- 1 file changed, 80 insertions(+), 55 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 12b4e20970..cdd8006931 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -2,15 +2,15 @@ Digital Zero-Noise Extrapolation with Catalyst ============================================== -In this tutorial, you will learn how to use Zero-Noise Extrapolation (ZNE) in combination with -Catalyst. We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum +In this tutorial, you will learn how to use error mitigation, and in particular +the Zero-Noise Extrapolation (ZNE) technique, in combination with Catalyst. +We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum simulator, and use extrapolation techniques to estimate the zero-noise result, all while leveraging just-in-time (JIT) compilation through `Catalyst `_. What is ZNE ----------- - Zero-Noise Extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum results by running circuits at varying noise levels and extrapolating back to a hypothetical @@ -41,9 +41,9 @@ Stage 3: Combining the results ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -After executing the noise-scaled circuits, we perform an extrapolation to estimate the zero-noise -limit---the result we would expect in a noise-free scenario. Catalyst provides two methods for -perfoming this extrapolation: +After executing the noise-scaled circuits, an extrapolation on the results is performed +to estimate the zero-noise limit---the result we would expect in a noise-free scenario. +Catalyst provides two methods for perfoming this extrapolation: 1. **Polynomial extrapolation**, and 2. **Exponential extrapolation**. @@ -61,21 +61,22 @@ Using ZNE in Catalyst --------------------- - -ZNE is also available for just-in-time (JIT) compilation of PennyLane programs, -starting from Catalyst v0.8.1. +On top of the error mitigation routines offered in Pennylane, ZNE is also available for just-in-time +(JIT) compilation, starting from Catalyst v0.8.1. In this tutorial we see how an error mitigation routine can be integrated in a Catalyst workflow. At the end of the tutorial, we will compare time for the execution of ZNE routines in -Pennylane vs. Catalyst. +pure Pennylane vs. Pennylane Catalyst with JIT. Mirror circuit -------------- -First we build a mirror-circuit starting off a unitary 2-design. +The first step for demoing an error mitigation routine is to define a circuit. +Here we build a simple mirror-circuit starting off a unitary 2-design. This is a typical construction for a randomized benchmarking circuit, which is used in many tasks in quantum computing. Given such circuit, we measure the expectation value :math:`\langle Z\rangle` -on the state of the first qubit. +on the state of the first qubit, and by construction of the circuit, we expect this value to be +equal to 1. """ import os @@ -101,8 +102,7 @@ def circuit(w1, w2): return qml.expval(qml.PauliZ(0)) ############################################################################## -# We execute the circuit on the Qrack simulator, first without noise, -# and then in a noisy scenario. +# As a sanity check, we first execute the circuit on the Qrack simulator without any noise. noiseless_device = qml.device("qrack.simulator", n_wires, isNoisy=False) @@ -110,8 +110,13 @@ def circuit(w1, w2): print(f"Ideal value: {ideal_value}") ############################################################################## -# As expected, in the noiseless scenario, the first qubit is in the :math:|0\rangle` -# state, so the expecation value of the Pauli-Z measurement is equal to 1. +# As expected, in the noiseless scenario, the expecation value of the Pauli-Z measurement +# is equal to 1, since the first qubit is back in the :math:`|0\rangle` state. +# +# Let's now run the circuit through a noisy scenario. The Qrack simulator models noise by +# applying single-qubit depolarizing noise channels to all qubits in all gates of the circuit. +# The probability of error is specified by the value of the +# `QRACK_GATE_DEPOLARIZATION` environment variable. NOISE_LEVEL = 0.01 os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) @@ -119,41 +124,44 @@ def circuit(w1, w2): noisy_qnode = qml.QNode(circuit, device=noisy_device) noisy_value = noisy_qnode(w1, w2) -print(f"Error without mitigation: {abs(ideal_value - noisy_value):.8f}") +print(f"Error without mitigation: {abs(ideal_value - noisy_value):.3f}") ############################################################################## -# With a circuit and simulator defined, we can begin to define some of the necessary parameters -# In particular we will need to specify: +# Again expected, we obtain a noisy value that diverges from the ideal value we obtained above. +# Fortunately, we have error mitigation to the rescue! We can apply ZNE, however we are still +# missing some necessary parameters. In particular we still need to specify: # -# 1. The noise scaling factors (i.e. how much to increase the depth of the circuit) -# 2. The _method_ for scaling this noise up (in Catalyst there are two options: `global` and +# 1. The method for scaling this noise up (in Catalyst there are two options: `global` and # `local`) -# 3. The extrapolation technique to use to estimate the ideal value (available in Catalyst are -# polynomial, and exponential extrapolation). +# 2. The noise scaling factors (i.e. how much to increase the depth of the circuit) +# 3. The extrapolation technique used to estimate the ideal value (available in Catalyst are +# polynomial and exponential extrapolation). # -# We'll define the scale factors as a `jax.numpy.array` where the scale factors represents -# the number of time the circuit is folded. +# First, we choose a method to scale the noise. This needs to be specified as a Python string. -scale_factors = [1, 3, 5] +folding_method = "global" ############################################################################## -# Next, we'll choose a method to scale the noise. This needs to be defined as a Python string. - -folding_method = "global" +# Next, we pick a list of scale factors. At the time of writing this tutorial, +# Catalyst supports only odd integer scale factors. In the global folding setting, +# a scale factor :math:`s` correspond to the circuit being folded +# :math:`\frac{s - 1}{2}` times. +scale_factors = [1, 3, 5] ############################################################################## -# Finally, we'll choose the extrapolation technique. Both exponential and polynoamial extrapolation -# is available in the `pennylane.transforms` module. Both of these functions can be passed directly -# into `mitigate_with_zne`! +# Finally, we'll choose the extrapolation technique. Both exponential and polynomial extrapolation +# is available in the `pennylane.transforms` module, and both of these functions can be passed directly +# into Catalyst's `mitigate_with_zne`. In this tutorial we use polynomial extrapolation, +# which we hypothesize it best models the behavior of the noise scenario we are considering. -from pennylane.transforms import richardson_extrapolate +from pennylane.transforms import poly_extrapolate +from functools import partial -extrapolation_method = richardson_extrapolate +extrapolation_method = partial(poly_extrapolate, order = 3) ############################################################################## # We're now ready to run our example using ZNE with Catalyst! Putting these all together we're able -# to define a very simple `QNode`! - +# to define a very simple `QNode`, which represents the mitigated version of the original circuit. @qjit def mitigated_circuit_qjit(w1, w2): @@ -169,11 +177,18 @@ def mitigated_circuit_qjit(w1, w2): print(f"Error with ZNE in Catalyst: {abs(ideal_value - zne_value):.3f}") - ############################################################################## -# ZNE in Pennylane -# -------------------------------------------------------- -# Notice +# It's crucial to note that we can use the `@qjit` decorator here, as all the functions used +# to define the node are compatible with Catalyst, and we can therefore +# exploit the potential of just-in-time compilation. +# +# ZNE in Pennylane without @qjit +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# For comparison, let's define a very similar `QNode`, but this time we don't decorate the node +# as just-in-time compilable. +# When it comes to the parameters, the only difference here (due to an implementation technicality) +# is the type of the `folding` argument. Despite the type being different, however, +# the value of the folding method is the same, i.e., global folding. def mitigated_circuit(w1, w2): return qml.transforms.mitigate_with_zne( @@ -187,9 +202,9 @@ def mitigated_circuit(w1, w2): print(f"Error with ZNE in Pennylane: {abs(ideal_value - zne_value):.3f}") -############################################################################## -# We use the timeit module in Python to measure execution time of a mitigated circuit -# ... +############################################################################## +# To showcase the impact of JIT compilation, let's use Python's timeit module +# to measure execution time of `mitigated_circuit_qjit` vs. `mitigated_circuit`. repeat = 5 # number of timing runs number = 5 # number of loops executed in each timing run @@ -198,40 +213,50 @@ def mitigated_circuit(w1, w2): "mitigated_circuit(w1, w2)", globals=globals(), number=number, repeat=repeat) -print(f"Pennylane running time (best of {repeat}): {min(times):.3f}") +print(f"mitigated_circuit running time (best of {repeat}): {min(times):.3f}s") times = timeit.repeat( "mitigated_circuit_qjit(w1, w2)", globals=globals(), number=number, repeat=repeat) -print(f"Catalyst running time (best of {repeat}): {min(times):.3f}") - - - +print(f"mitigated_circuit_qjit running time (best of {repeat}): {min(times):.3f}s") ############################################################################## -# Here is a recap of the landscape of QEM techniques available in Pennylane. +# Already with the simple circuit we started with, and with the simple parameters in our example, +# we can appreciate the performance differences. That was at the cost of very minimal syntax change. +# +# There are still reasons to use ZNE in Pennylane without @qjit, for instance, +# whenever the device of choice is not supported by Catalyst. To help the users orient themselves, +# we conlcude with a landscape of the QEM techniques available on Pennylane. # # .. list-table:: -# :widths: 30 35 20 15 +# :widths: 30 20 20 20 20 30 # :header-rows: 1 # # * - **Framework** -# - **Techniques** +# - **ZNE folding** +# - **ZNE extrapolation** # - **Differentiable** # - **JIT** +# - **other QEM techniques** # * - Pennylane + Mitiq -# - ZNE, PEC, CDR, DDD, REM +# - global, local, random +# - polynomial, exponential # - # - +# - PEC, CDR, DDD, REM # * - Pennylane transforms -# - ZNE +# - global, local +# - polynomial, exponential # - ✅ # - -# * - Catalyst -# - ZNE +# - +# * - Catalyst (experimental) +# - global, local +# - polynomial, exponential # - ✅ # - ✅ +# - ############################################################################## From 2e64d807a7ea685ca2a43cf2faa2754c0d7fb7e9 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 19 Sep 2024 18:20:37 +0200 Subject: [PATCH 14/44] reformat demo with black --- demonstrations/tutorial_zne_catalyst.py | 56 +++++++++++++------------ pyproject.toml | 1 + 2 files changed, 30 insertions(+), 27 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index cdd8006931..29a4f3f7c6 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -101,6 +101,7 @@ def circuit(w1, w2): qml.adjoint(template)(w1, w2, wires=range(n_wires)) return qml.expval(qml.PauliZ(0)) + ############################################################################## # As a sanity check, we first execute the circuit on the Qrack simulator without any noise. @@ -110,13 +111,13 @@ def circuit(w1, w2): print(f"Ideal value: {ideal_value}") ############################################################################## -# As expected, in the noiseless scenario, the expecation value of the Pauli-Z measurement +# As expected, in the noiseless scenario, the expecation value of the Pauli-Z measurement # is equal to 1, since the first qubit is back in the :math:`|0\rangle` state. # # Let's now run the circuit through a noisy scenario. The Qrack simulator models noise by # applying single-qubit depolarizing noise channels to all qubits in all gates of the circuit. -# The probability of error is specified by the value of the -# `QRACK_GATE_DEPOLARIZATION` environment variable. +# The probability of error is specified by the value of the +# `QRACK_GATE_DEPOLARIZATION` environment variable. NOISE_LEVEL = 0.01 os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) @@ -128,7 +129,7 @@ def circuit(w1, w2): ############################################################################## # Again expected, we obtain a noisy value that diverges from the ideal value we obtained above. -# Fortunately, we have error mitigation to the rescue! We can apply ZNE, however we are still +# Fortunately, we have error mitigation to the rescue! We can apply ZNE, however we are still # missing some necessary parameters. In particular we still need to specify: # # 1. The method for scaling this noise up (in Catalyst there are two options: `global` and @@ -142,8 +143,8 @@ def circuit(w1, w2): folding_method = "global" ############################################################################## -# Next, we pick a list of scale factors. At the time of writing this tutorial, -# Catalyst supports only odd integer scale factors. In the global folding setting, +# Next, we pick a list of scale factors. At the time of writing this tutorial, +# Catalyst supports only odd integer scale factors. In the global folding setting, # a scale factor :math:`s` correspond to the circuit being folded # :math:`\frac{s - 1}{2}` times. scale_factors = [1, 3, 5] @@ -151,18 +152,19 @@ def circuit(w1, w2): ############################################################################## # Finally, we'll choose the extrapolation technique. Both exponential and polynomial extrapolation # is available in the `pennylane.transforms` module, and both of these functions can be passed directly -# into Catalyst's `mitigate_with_zne`. In this tutorial we use polynomial extrapolation, +# into Catalyst's `mitigate_with_zne`. In this tutorial we use polynomial extrapolation, # which we hypothesize it best models the behavior of the noise scenario we are considering. from pennylane.transforms import poly_extrapolate from functools import partial -extrapolation_method = partial(poly_extrapolate, order = 3) +extrapolation_method = partial(poly_extrapolate, order=3) ############################################################################## # We're now ready to run our example using ZNE with Catalyst! Putting these all together we're able # to define a very simple `QNode`, which represents the mitigated version of the original circuit. + @qjit def mitigated_circuit_qjit(w1, w2): return mitigate_with_zne( @@ -178,8 +180,8 @@ def mitigated_circuit_qjit(w1, w2): print(f"Error with ZNE in Catalyst: {abs(ideal_value - zne_value):.3f}") ############################################################################## -# It's crucial to note that we can use the `@qjit` decorator here, as all the functions used -# to define the node are compatible with Catalyst, and we can therefore +# It's crucial to note that we can use the `@qjit` decorator here, as all the functions used +# to define the node are compatible with Catalyst, and we can therefore # exploit the potential of just-in-time compilation. # # ZNE in Pennylane without @qjit @@ -188,7 +190,8 @@ def mitigated_circuit_qjit(w1, w2): # as just-in-time compilable. # When it comes to the parameters, the only difference here (due to an implementation technicality) # is the type of the `folding` argument. Despite the type being different, however, -# the value of the folding method is the same, i.e., global folding. +# the value of the folding method is the same, i.e., global folding. + def mitigated_circuit(w1, w2): return qml.transforms.mitigate_with_zne( @@ -198,36 +201,35 @@ def mitigated_circuit(w1, w2): folding=qml.transforms.fold_global, )(w1, w2) + zne_value = mitigated_circuit(w1, w2) print(f"Error with ZNE in Pennylane: {abs(ideal_value - zne_value):.3f}") -############################################################################## +############################################################################## # To showcase the impact of JIT compilation, let's use Python's timeit module # to measure execution time of `mitigated_circuit_qjit` vs. `mitigated_circuit`. repeat = 5 # number of timing runs -number = 5 # number of loops executed in each timing run +number = 5 # number of loops executed in each timing run -times = timeit.repeat( - "mitigated_circuit(w1, w2)", - globals=globals(), number=number, repeat=repeat) +times = timeit.repeat("mitigated_circuit(w1, w2)", globals=globals(), number=number, repeat=repeat) print(f"mitigated_circuit running time (best of {repeat}): {min(times):.3f}s") times = timeit.repeat( - "mitigated_circuit_qjit(w1, w2)", - globals=globals(), number=number, repeat=repeat) + "mitigated_circuit_qjit(w1, w2)", globals=globals(), number=number, repeat=repeat +) print(f"mitigated_circuit_qjit running time (best of {repeat}): {min(times):.3f}s") ############################################################################## # Already with the simple circuit we started with, and with the simple parameters in our example, -# we can appreciate the performance differences. That was at the cost of very minimal syntax change. +# we can appreciate the performance differences. That was at the cost of very minimal syntax change. # -# There are still reasons to use ZNE in Pennylane without @qjit, for instance, -# whenever the device of choice is not supported by Catalyst. To help the users orient themselves, -# we conlcude with a landscape of the QEM techniques available on Pennylane. +# There are still reasons to use ZNE in Pennylane without @qjit, for instance, +# whenever the device of choice is not supported by Catalyst. To help the users orient themselves, +# we conlcude with a landscape of the QEM techniques available on Pennylane. # # .. list-table:: # :widths: 30 20 20 20 20 30 @@ -242,21 +244,21 @@ def mitigated_circuit(w1, w2): # * - Pennylane + Mitiq # - global, local, random # - polynomial, exponential -# - -# - +# - +# - # - PEC, CDR, DDD, REM # * - Pennylane transforms # - global, local # - polynomial, exponential # - ✅ -# - -# - +# - +# - # * - Catalyst (experimental) # - global, local # - polynomial, exponential # - ✅ # - ✅ -# - +# - ############################################################################## diff --git a/pyproject.toml b/pyproject.toml index 2307a9ed74..f860e50ba4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,6 +43,7 @@ pennylane-cirq = "0.36.0" pennylane-qiskit = "0.38.0" pennylane-qulacs = "0.36.0" pennylane-catalyst = "0.8.1" +pennylane-qrack = "0.9.1" ########################################################## From 5d44c2026bf3bde99e92399e4fbf50faae4c62b2 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 19 Sep 2024 18:55:45 +0200 Subject: [PATCH 15/44] lock poetry file --- poetry.lock | 1363 +++++++++++++++++++++++++++------------------------ 1 file changed, 721 insertions(+), 642 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7675ebbc6a..c951fc7a55 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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update + sudo apt install ocl-icd-opencl-dev + - name: Download Worker Load Data Artifact uses: actions/download-artifact@v3 with: From 6ff56aaf8714238760d9dcddf798d9c00dda567c Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Tue, 24 Sep 2024 19:40:44 +0200 Subject: [PATCH 17/44] disable opencl on qrack device --- demonstrations/tutorial_zne_catalyst.py | 4 +- poetry.lock | 800 ++++++++++++------------ 2 files changed, 409 insertions(+), 395 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 29a4f3f7c6..a233978568 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -105,7 +105,7 @@ def circuit(w1, w2): ############################################################################## # As a sanity check, we first execute the circuit on the Qrack simulator without any noise. -noiseless_device = qml.device("qrack.simulator", n_wires, isNoisy=False) +noiseless_device = qml.device("qrack.simulator", n_wires, isNoisy=False, isOpenCL=False) ideal_value = qml.QNode(circuit, device=noiseless_device)(w1, w2) print(f"Ideal value: {ideal_value}") @@ -121,7 +121,7 @@ def circuit(w1, w2): NOISE_LEVEL = 0.01 os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) -noisy_device = qml.device("qrack.simulator", n_wires, isNoisy=True) +noisy_device = qml.device("qrack.simulator", n_wires, isNoisy=True, isOpenCL=False) noisy_qnode = qml.QNode(circuit, device=noisy_device) noisy_value = noisy_qnode(w1, w2) diff --git a/poetry.lock b/poetry.lock index c951fc7a55..5fa6ea6472 100644 --- a/poetry.lock +++ b/poetry.lock @@ -145,13 +145,13 @@ files = [ [[package]] name = "alembic" -version = "1.13.2" +version = "1.13.3" description = "A database migration tool for SQLAlchemy." optional = false python-versions = ">=3.8" files = [ - {file = "alembic-1.13.2-py3-none-any.whl", hash = "sha256:6b8733129a6224a9a711e17c99b08462dbf7cc9670ba8f2e2ae9af860ceb1953"}, - {file = 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++++++++++++++++++- pyproject.toml | 5 +++-- 2 files changed, 21 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index 5fa6ea6472..34dadfe82f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -359,6 +359,23 @@ files = [ {file = "bidict-0.23.1.tar.gz", hash = "sha256:03069d763bc387bbd20e7d49914e75fc4132a41937fa3405417e1a5a2d006d71"}, ] +[[package]] +name = "bluequbit" +version = "0.8.1b1" +description = "Python SDK to BlueQubit app" +optional = false +python-versions = ">=3.8" +files = [ + {file = "bluequbit-0.8.1b1-py3-none-any.whl", hash = "sha256:0c6bd231f46db2a336fe1de0498f9faeafe2a3d13e8efda5e9568615c127e2a7"}, + {file = "bluequbit-0.8.1b1.tar.gz", hash = "sha256:832f9c5b3f50c594659e470af5fda3fd33b0ebd5a3c5af40922db94eba314b86"}, +] + +[package.dependencies] +numpy = ">=1.21,<2.0" +packaging = "*" +python-dateutil = ">=2.8,<3.0" +requests = ">=2.28,<3.0" + [[package]] name = "cachetools" version = "5.5.0" @@ -8070,4 +8087,4 @@ files = [ [metadata] lock-version = 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'darwin' and platform_machine == 'arm64'" }, + { version = "0.16.2", source = "PyPI", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'" }, { version = "0.16.2+cpu", source = "pytorch-cpu", markers = "platform_machine == 'x86_64'" } ] From a352984a640a423bf2a0072fdea0b544a442abfc Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 26 Sep 2024 18:27:57 +0200 Subject: [PATCH 19/44] bump qrack --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a1f2d109d0..f3eb1d85e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,7 @@ pennylane-cirq = "0.36.0" pennylane-qiskit = "0.38.0" pennylane-qulacs = "0.36.0" pennylane-catalyst = "0.8.1" -pennylane-qrack = "0.9.1" +pennylane-qrack = "0.9.2" ########################################################## From 9236c62c840a468f529b9e434e4f601356785533 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 26 Sep 2024 18:31:38 +0200 Subject: [PATCH 20/44] update poetry lock --- poetry.lock | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/poetry.lock b/poetry.lock index 2617863b40..6f8aed3344 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4276,19 +4276,19 @@ sympy = "<1.13" [[package]] name = "pennylane-qrack" -version = "0.9.1" +version = "0.9.2" description = "PennyLane plugin for Qrack." optional = false python-versions = "*" files = [ - {file = "pennylane_qrack-0.9.1-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:68313deba011d0e473963c14b1e0288d96768c861db0a2383ed0397e7b87353c"}, - {file = "pennylane_qrack-0.9.1-py3-none-macosx_13_0_x86_64.whl", hash = "sha256:edfe61389611cd00dec8b2ba5037742c0d051d40d26f066c334980529da39bf0"}, - {file = "pennylane_qrack-0.9.1-py3-none-macosx_14_0_arm64.whl", hash = "sha256:a2bd4c617f5a4a2c47a903a310f6a8d7448867965dcba4d861d9b176c927100a"}, - {file = "pennylane_qrack-0.9.1-py3-none-manylinux_2_31_x86_64.whl", hash = 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pennylane-catalyst = "0.8.1" -pennylane-qrack = "0.9.2" +pennylane-qrack = "0.9.3" ########################################################## From bc929d8a464493594a2f1dad6a74cc11bface991 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Fri, 27 Sep 2024 10:08:56 +0200 Subject: [PATCH 22/44] new day, new run --- poetry.lock | 20 ++++++++++---------- pyproject.toml | 2 +- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/poetry.lock b/poetry.lock index bb609eb1eb..7afe869732 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4276,19 +4276,19 @@ sympy = "<1.13" [[package]] name = "pennylane-qrack" -version = "0.9.3" +version = "0.9.4" description = "PennyLane plugin for Qrack." optional = false python-versions = "*" files = [ - {file = "pennylane_qrack-0.9.3-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:3eb43aa00fb590b901a4ebe4d8ec330fc7b983f12496d533481dc36cdf0686cb"}, - {file = "pennylane_qrack-0.9.3-py3-none-macosx_13_0_x86_64.whl", hash = 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23/44] pin pyqrack version --- poetry.lock | 20 ++++++++++---------- pyproject.toml | 1 + 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7afe869732..60490e78df 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4870,19 +4870,19 @@ files = [ [[package]] name = "pyqrack" -version = "1.30.25" +version = "1.30.30" description = "pyqrack - Pure Python vm6502q/qrack Wrapper" optional = false python-versions = "*" files = [ - {file = "pyqrack-1.30.25-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:34f8196129892c870aeba2039e3779816a5b8302814d9329a98d77ebc856902f"}, - {file = "pyqrack-1.30.25-py3-none-macosx_13_0_x86_64.whl", hash = "sha256:7c92fbfedcf58c993b2893533424fa66c624999defd1f2cdd71acf24b7dde2ee"}, - {file = "pyqrack-1.30.25-py3-none-macosx_14_0_arm64.whl", hash = "sha256:4055db026fe7ccf193c3805e822cfbfd64f93b4b66964b691ea2cd18213bc6af"}, - {file = "pyqrack-1.30.25-py3-none-manylinux_2_31_x86_64.whl", hash = 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"7371c73563967d6548b8732f9b70c7bab9c256a1bfaef93223543f59c6ff73aa" diff --git a/pyproject.toml b/pyproject.toml index 3ead16df80..daeddd27b7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,7 @@ pennylane-qiskit = "0.38.0" pennylane-qulacs = "0.36.0" pennylane-catalyst = "0.8.1" pennylane-qrack = "0.9.4" +pyqrack = "1.30.30" ########################################################## From c6f704ed26c05559be28bd743d9eee403ba3c7d2 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 3 Oct 2024 15:48:51 +0200 Subject: [PATCH 24/44] Update demonstrations/tutorial_zne_catalyst.py Co-authored-by: Josh Izaac --- demonstrations/tutorial_zne_catalyst.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index a233978568..de711a3690 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -56,7 +56,7 @@ ZNE in particular is also offered out of the box in Pennylane as a *differentiable* error mitigation technique, for usage in combination with variational workflows. More on this in the tutorial -`Differentiating quantum error mitigation transforms `_. +:doc:`Differentiating quantum error mitigation transforms `. Using ZNE in Catalyst From b5e4158bbbad0a949238bf460fe084ae4c2a7cd4 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 3 Oct 2024 15:50:45 +0200 Subject: [PATCH 25/44] Update demonstrations/tutorial_zne_catalyst.py Co-authored-by: Josh Izaac --- demonstrations/tutorial_zne_catalyst.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index de711a3690..eb5f0c7dd8 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -227,7 +227,7 @@ def mitigated_circuit(w1, w2): # Already with the simple circuit we started with, and with the simple parameters in our example, # we can appreciate the performance differences. That was at the cost of very minimal syntax change. # -# There are still reasons to use ZNE in Pennylane without @qjit, for instance, +# There are still reasons to use ZNE in Pennylane without :func:`~.qjit`, for instance, # whenever the device of choice is not supported by Catalyst. To help the users orient themselves, # we conlcude with a landscape of the QEM techniques available on Pennylane. # From 09c27283105961a20214c63ca765cd7d1eac53a2 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 3 Oct 2024 16:00:44 +0200 Subject: [PATCH 26/44] Apply suggestions from Josh's code review Only once I finally figured out I could apply them in batch from the Files tab :D Co-authored-by: Josh Izaac --- demonstrations/tutorial_zne_catalyst.py | 105 ++++++++++++------------ 1 file changed, 53 insertions(+), 52 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index eb5f0c7dd8..075b702751 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -3,11 +3,12 @@ ============================================== In this tutorial, you will learn how to use error mitigation, and in particular -the Zero-Noise Extrapolation (ZNE) technique, in combination with Catalyst. +the Zero-Noise Extrapolation (ZNE) technique, in combination with `Catalyst `_, a framework for quantum +just-in-time (JIT) compilation with PennyLane. We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum simulator, and use extrapolation techniques to estimate the zero-noise result, all while -leveraging just-in-time (JIT) compilation through -`Catalyst `_. +leveraging JIT compilation through +Catalyst. What is ZNE ----------- @@ -36,7 +37,7 @@ quantum hardware or a noisy quantum simulator. In this tutorial, we'll use the `Qrack quantum simulator `_, which is both compatible with Catalyst, and implements a noise model. For more about the integration of Qrack and Catalyst, see -the demo `QJIT compilation with Qrack and Catalyst `_. +the demo :doc:`QJIT compilation with Qrack and Catalyst `. Stage 3: Combining the results ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -50,17 +51,15 @@ Using ZNE with Pennylane ------------------------ -The demo `Error mitigation with Mitiq and PennyLane `_ -shows how ZNE, along with other error mitigation techniques, can be carried out in Pennylane by using Mitiq, + +The demo :doc:`Error mitigation with Mitiq and PennyLane ` +shows how ZNE, along with other error mitigation techniques, can be carried out in Pennylane by using `Mitiq `__, a Python library developed by Unitary Fund. ZNE in particular is also offered out of the box in Pennylane as a *differentiable* error mitigation technique, for usage in combination with variational workflows. More on this in the tutorial :doc:`Differentiating quantum error mitigation transforms `. - -Using ZNE in Catalyst ---------------------- On top of the error mitigation routines offered in Pennylane, ZNE is also available for just-in-time (JIT) compilation, starting from Catalyst v0.8.1. In this tutorial we see how an error mitigation routine can be integrated in a Catalyst workflow. @@ -68,8 +67,8 @@ At the end of the tutorial, we will compare time for the execution of ZNE routines in pure Pennylane vs. Pennylane Catalyst with JIT. -Mirror circuit --------------- +Defining the mirror circuit +--------------------------- The first step for demoing an error mitigation routine is to define a circuit. Here we build a simple mirror-circuit starting off a unitary 2-design. @@ -83,7 +82,7 @@ import timeit import pennylane as qml -from pennylane import numpy as np +import numpy as np from catalyst import qjit, mitigate_with_zne n_wires = 5 @@ -114,6 +113,8 @@ def circuit(w1, w2): # As expected, in the noiseless scenario, the expecation value of the Pauli-Z measurement # is equal to 1, since the first qubit is back in the :math:`|0\rangle` state. # +# Mitigating the noisy circuit +# ---------------------------- # Let's now run the circuit through a noisy scenario. The Qrack simulator models noise by # applying single-qubit depolarizing noise channels to all qubits in all gates of the circuit. # The probability of error is specified by the value of the @@ -132,8 +133,8 @@ def circuit(w1, w2): # Fortunately, we have error mitigation to the rescue! We can apply ZNE, however we are still # missing some necessary parameters. In particular we still need to specify: # -# 1. The method for scaling this noise up (in Catalyst there are two options: `global` and -# `local`) +# 1. The method for scaling this noise up (in Catalyst there are two options: ``global`` and +# ``local``) # 2. The noise scaling factors (i.e. how much to increase the depth of the circuit) # 3. The extrapolation technique used to estimate the ideal value (available in Catalyst are # polynomial and exponential extrapolation). @@ -151,8 +152,8 @@ def circuit(w1, w2): ############################################################################## # Finally, we'll choose the extrapolation technique. Both exponential and polynomial extrapolation -# is available in the `pennylane.transforms` module, and both of these functions can be passed directly -# into Catalyst's `mitigate_with_zne`. In this tutorial we use polynomial extrapolation, +# is available in the :mod:`qml.transforms ` module, and both of these functions can be passed directly +# into Catalyst's :func:`catalyst.mitigate_with_zne` function. In this tutorial we use polynomial extrapolation, # which we hypothesize it best models the behavior of the noise scenario we are considering. from pennylane.transforms import poly_extrapolate @@ -162,7 +163,7 @@ def circuit(w1, w2): ############################################################################## # We're now ready to run our example using ZNE with Catalyst! Putting these all together we're able -# to define a very simple `QNode`, which represents the mitigated version of the original circuit. +# to define a very simple :func:`~.QNode`, which represents the mitigated version of the original circuit. @qjit @@ -180,16 +181,16 @@ def mitigated_circuit_qjit(w1, w2): print(f"Error with ZNE in Catalyst: {abs(ideal_value - zne_value):.3f}") ############################################################################## -# It's crucial to note that we can use the `@qjit` decorator here, as all the functions used +# It's crucial to note that we can use the :func:`~.qjit` decorator here, as all the functions used # to define the node are compatible with Catalyst, and we can therefore # exploit the potential of just-in-time compilation. # -# ZNE in Pennylane without @qjit -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# For comparison, let's define a very similar `QNode`, but this time we don't decorate the node +# Benchmarking +# ------------ +# For comparison, let's define a very similar :func:`~.qnode`, but this time we don't decorate the node # as just-in-time compilable. # When it comes to the parameters, the only difference here (due to an implementation technicality) -# is the type of the `folding` argument. Despite the type being different, however, +# is the type of the ``folding`` argument. Despite the type being different, however, # the value of the folding method is the same, i.e., global folding. @@ -207,8 +208,8 @@ def mitigated_circuit(w1, w2): print(f"Error with ZNE in Pennylane: {abs(ideal_value - zne_value):.3f}") ############################################################################## -# To showcase the impact of JIT compilation, let's use Python's timeit module -# to measure execution time of `mitigated_circuit_qjit` vs. `mitigated_circuit`. +# To showcase the impact of JIT compilation, let's use Python's ``timeit`` module +# to measure execution time of ``mitigated_circuit_qjit`` vs. ``mitigated_circuit``: repeat = 5 # number of timing runs number = 5 # number of loops executed in each timing run @@ -228,37 +229,37 @@ def mitigated_circuit(w1, w2): # we can appreciate the performance differences. That was at the cost of very minimal syntax change. # # There are still reasons to use ZNE in Pennylane without :func:`~.qjit`, for instance, -# whenever the device of choice is not supported by Catalyst. To help the users orient themselves, +# whenever the device of choice is not supported by Catalyst. To help, # we conlcude with a landscape of the QEM techniques available on Pennylane. # -# .. list-table:: -# :widths: 30 20 20 20 20 30 -# :header-rows: 1 +# .. list-table:: +# :widths: 30 20 20 20 20 30 +# :header-rows: 1 # -# * - **Framework** -# - **ZNE folding** -# - **ZNE extrapolation** -# - **Differentiable** -# - **JIT** -# - **other QEM techniques** -# * - Pennylane + Mitiq -# - global, local, random -# - polynomial, exponential -# - -# - -# - PEC, CDR, DDD, REM -# * - Pennylane transforms -# - global, local -# - polynomial, exponential -# - ✅ -# - -# - -# * - Catalyst (experimental) -# - global, local -# - polynomial, exponential -# - ✅ -# - ✅ -# - +# * - **Framework** +# - **ZNE folding** +# - **ZNE extrapolation** +# - **Differentiable** +# - **JIT** +# - **other QEM techniques** +# * - Pennylane + Mitiq +# - global, local, random +# - polynomial, exponential +# - +# - +# - PEC, CDR, DDD, REM +# * - Pennylane transforms +# - global, local +# - polynomial, exponential +# - ✅ +# - +# - +# * - Catalyst (experimental) +# - global, local +# - polynomial, exponential +# - ✅ +# - ✅ +# - ############################################################################## From e5359ad3340fd32766bbea0a776f945c4859f075 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 3 Oct 2024 18:19:20 +0200 Subject: [PATCH 27/44] add myself as an author --- _static/authors/alessandro_cosentino.png | Bin 0 -> 479341 bytes _static/authors/alessandro_cosentino.txt | 7 ++++++ .../tutorial_zne_catalyst.metadata.json | 4 ++++ demonstrations/tutorial_zne_catalyst.py | 21 ++++++++--------- poetry.lock | 22 +++++++++--------- pyproject.toml | 4 ++-- 6 files changed, 34 insertions(+), 24 deletions(-) create mode 100644 _static/authors/alessandro_cosentino.png create mode 100644 _static/authors/alessandro_cosentino.txt diff --git a/_static/authors/alessandro_cosentino.png b/_static/authors/alessandro_cosentino.png new file mode 100644 index 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We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum simulator, and use extrapolation techniques to estimate the zero-noise result, all while -leveraging JIT compilation through -Catalyst. +leveraging JIT compilation through Catalyst. What is ZNE ----------- -Zero-Noise Extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum +Zero-noise extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum results by running circuits at varying noise levels and extrapolating back to a hypothetical zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail, let's first @@ -44,10 +44,7 @@ After executing the noise-scaled circuits, an extrapolation on the results is performed to estimate the zero-noise limit---the result we would expect in a noise-free scenario. -Catalyst provides two methods for perfoming this extrapolation: - -1. **Polynomial extrapolation**, and -2. **Exponential extrapolation**. +Catalyst provides **polynomial** and **exponential** extrapolation methods. Using ZNE with Pennylane ------------------------ @@ -278,6 +275,8 @@ def mitigated_circuit(w1, w2): # ############################################################################## -# About the author -# ---------------- +# About the authors +# ----------------- +# .. include:: ../_static/authors/alessandro_cosentino.txt +# # .. include:: ../_static/authors/nate_stemen.txt diff --git a/poetry.lock b/poetry.lock index 60490e78df..549834962d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4276,25 +4276,25 @@ sympy = "<1.13" [[package]] name = "pennylane-qrack" -version = "0.9.4" +version = "0.9.7" description = "PennyLane plugin for Qrack." optional = false python-versions = "*" files = [ - {file = "pennylane_qrack-0.9.4-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:f4dbe35ab6f6abacc0d03f05afeaf595150509811df8a35fff9a345ca44c0554"}, - {file = "pennylane_qrack-0.9.4-py3-none-macosx_13_0_x86_64.whl", hash = "sha256:54c2b9f1f1ab398fdc37e41b64c76dde758a7506cd0220e955f7ec38274a1f20"}, - {file = "pennylane_qrack-0.9.4-py3-none-macosx_14_0_arm64.whl", hash = "sha256:1ccdfa3e3f075bdbe66296861b1fcb79bba28a82a29273e32c7b083c5bd24dc6"}, - {file = "pennylane_qrack-0.9.4-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:213431404fa319aea21e45c8253c9a3ec166168185afcafbfe9bdbdc14598932"}, - {file = "pennylane_qrack-0.9.4-py3-none-manylinux_2_35_x86_64.whl", hash = "sha256:dee67fe2d8deb8866a930819f8052109132b73435955cf310c0ef8e1d1cb6c1c"}, - {file = "pennylane_qrack-0.9.4-py3-none-manylinux_2_39_x86_64.whl", hash = "sha256:2b21ad77d9566643cecf7f11f6e49db85bd00db4f198e17536f9aa4d28a478a7"}, - {file = "pennylane_qrack-0.9.4-py3-none-win_amd64.whl", hash = "sha256:c279ee3956f5f3c085392fa0b8bfcb7cc2b0d6859b8247bfe53f88ebe0ab826f"}, - {file = "pennylane_qrack-0.9.4.tar.gz", hash = "sha256:03335072fcc4b2a8140317fe747b8b89cdf767a978460d148cfaa26b5979946a"}, + {file = "pennylane_qrack-0.9.7-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:2677c944b939370a642d15973ddd0d3dc8e10e951a949af60a1a66325a8c2b45"}, + {file = "pennylane_qrack-0.9.7-py3-none-macosx_13_0_x86_64.whl", hash = "sha256:0e0783c8e392f2cbc5d2b6c868cecb19f79e0bc20b57846dbb3e1c5930344609"}, + {file = "pennylane_qrack-0.9.7-py3-none-macosx_14_0_arm64.whl", hash = "sha256:6efc8de2666ec512069e6a8ea9e3d694d21a56741747822b67b0265edfad154e"}, + {file = "pennylane_qrack-0.9.7-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:4224b57e312051afa0dc81bec1085e39d70bad1ebc9b87e6dce39da30eaf4202"}, + {file = "pennylane_qrack-0.9.7-py3-none-manylinux_2_35_x86_64.whl", hash = "sha256:aa3b00e5d1a5430593719ff55e5b3ab9998e7105f550851a0511d887e74c3905"}, + {file = "pennylane_qrack-0.9.7-py3-none-manylinux_2_39_x86_64.whl", hash = "sha256:5afc34a8b497eedf10b4a41003548ec6b51d197651906c164b97415ccf9546bf"}, + {file = "pennylane_qrack-0.9.7-py3-none-win_amd64.whl", hash = "sha256:15dfd9a72474bac7aa3550856928947c559ced8869a43eab38ff243a733933ec"}, + {file = "pennylane_qrack-0.9.7.tar.gz", hash = "sha256:ef928cc9ec54499d0a8dea0c918b8ba18be651aa9e687f8c6d093fe30a11c4a3"}, ] [package.dependencies] numpy = ">=1.16" pennylane = ">=0.32" -pyqrack = ">=1.28.0" +pyqrack = ">=1.30.0" [[package]] name = "pennylane-qulacs" @@ -8087,4 +8087,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "~3.10.0" -content-hash = "7371c73563967d6548b8732f9b70c7bab9c256a1bfaef93223543f59c6ff73aa" +content-hash = "bf95b620bf493a9d65469a0493cf74139dbb782c12be09ae57aad0f3c23747d5" diff --git a/pyproject.toml b/pyproject.toml index daeddd27b7..1bd48e8104 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,8 +43,6 @@ pennylane-cirq = "0.36.0" pennylane-qiskit = "0.38.0" pennylane-qulacs = "0.36.0" pennylane-catalyst = "0.8.1" -pennylane-qrack = "0.9.4" -pyqrack = "1.30.30" ########################################################## @@ -67,6 +65,8 @@ optax = "0.2.3" flax = "0.9.0" qutip = "4.7.3" mitiq = "0.32.0" +pennylane-qrack = "0.9.7" +pyqrack = "1.30.30" zstd = "*" dill = "*" stim = "*" From 19db7756b6db1544df5162a80f98d5bee5662bcd Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 3 Oct 2024 18:24:31 +0200 Subject: [PATCH 28/44] fix bio --- _static/authors/alessandro_cosentino.txt | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/_static/authors/alessandro_cosentino.txt b/_static/authors/alessandro_cosentino.txt index aab7dc5574..3470413dad 100644 --- a/_static/authors/alessandro_cosentino.txt +++ b/_static/authors/alessandro_cosentino.txt @@ -1,7 +1,4 @@ .. bio:: Alessandro Cosentino :photo: ../_static/authors/alessandro_cosentino.png - Alessandro is a Member of Technical Staff at Unitary Fund. - He holds a PhD in Computer Science from the University of Waterloo with a thesis - in quantum information theory and optimization. - He is an open source software advocate with contributions to both classical and quantum projects. + Alessandro is a Member of Technical Staff at Unitary Fund. He holds a PhD in Computer Science from the University of Waterloo with a thesis in quantum information theory and optimization. He is an open source software advocate with contributions to both classical and quantum projects. From 14d83a346931479765ff759309778151dcda76b7 Mon Sep 17 00:00:00 2001 From: WrathfulSpatula Date: Sat, 5 Oct 2024 12:13:36 -0400 Subject: [PATCH 29/44] Numeric noise constructor param. for Qrack --- demonstrations/tutorial_zne_catalyst.py | 9 ++++----- pyproject.toml | 4 ++-- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index b31df8b930..d063a93abe 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -101,7 +101,7 @@ def circuit(w1, w2): ############################################################################## # As a sanity check, we first execute the circuit on the Qrack simulator without any noise. -noiseless_device = qml.device("qrack.simulator", n_wires, isNoisy=False, isOpenCL=False) +noiseless_device = qml.device("qrack.simulator", n_wires, noise=0) ideal_value = qml.QNode(circuit, device=noiseless_device)(w1, w2) print(f"Ideal value: {ideal_value}") @@ -114,12 +114,11 @@ def circuit(w1, w2): # ---------------------------- # Let's now run the circuit through a noisy scenario. The Qrack simulator models noise by # applying single-qubit depolarizing noise channels to all qubits in all gates of the circuit. -# The probability of error is specified by the value of the -# `QRACK_GATE_DEPOLARIZATION` environment variable. +# The probability of error is specified by the value of the `noise` constructor argument (or +# the `QRACK_GATE_DEPOLARIZATION` environment variable). NOISE_LEVEL = 0.01 -os.environ["QRACK_GATE_DEPOLARIZATION"] = str(NOISE_LEVEL) -noisy_device = qml.device("qrack.simulator", n_wires, isNoisy=True, isOpenCL=False) +noisy_device = qml.device("qrack.simulator", n_wires, noise=NOISE_LEVEL) noisy_qnode = qml.QNode(circuit, device=noisy_device) noisy_value = noisy_qnode(w1, w2) diff --git a/pyproject.toml b/pyproject.toml index 1bd48e8104..348719b824 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,8 +65,8 @@ optax = "0.2.3" flax = "0.9.0" qutip = "4.7.3" mitiq = "0.32.0" -pennylane-qrack = "0.9.7" -pyqrack = "1.30.30" +pennylane-qrack = "0.10.0" +pyqrack = "1.31.0" zstd = "*" dill = "*" stim = "*" From 3e5eb5e537fd6ac5e4d43dfa594dabb799b65147 Mon Sep 17 00:00:00 2001 From: WrathfulSpatula Date: Sat, 5 Oct 2024 12:42:35 -0400 Subject: [PATCH 30/44] Fix poetry.lock --- poetry.lock | 50 +++++++++++++++++++++++++++++++------------------- 1 file changed, 31 insertions(+), 19 deletions(-) diff --git a/poetry.lock b/poetry.lock index 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"5c524ac9de627f02beb9b749212a8804ae8a0be9f9e83f393a0f2f7114dd86af" From dbc7c19d08a05486d9df9e8fcc13b3e858b63aa0 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Mon, 7 Oct 2024 15:13:12 +0200 Subject: [PATCH 31/44] address more review comments --- .../tutorial_zne_catalyst.metadata.json | 2 +- demonstrations/tutorial_zne_catalyst.py | 47 +++++++++---------- poetry.lock | 12 ----- 3 files changed, 24 insertions(+), 37 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.metadata.json b/demonstrations/tutorial_zne_catalyst.metadata.json index cd20da9888..cde76b979c 100644 --- a/demonstrations/tutorial_zne_catalyst.metadata.json +++ b/demonstrations/tutorial_zne_catalyst.metadata.json @@ -1,5 +1,5 @@ { - "title": "Digital Zero-Noise Extrapolation with Catalyst", + "title": "Digital zero-noise extrapolation with Catalyst", "authors": [ { "id": "alessandro_cosentino", diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index d063a93abe..eed12a7601 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -1,5 +1,5 @@ r""" -Digital zero-noise Extrapolation with Catalyst +Digital zero-noise extrapolation with Catalyst ============================================== In this tutorial, you will learn how to use error mitigation, and in particular @@ -10,9 +10,27 @@ simulator, and use extrapolation techniques to estimate the zero-noise result, all while leveraging JIT compilation through Catalyst. +Using ZNE with Pennylane +------------------------ + +The demo :doc:`Error mitigation with Mitiq and PennyLane ` +shows how ZNE, along with other error mitigation techniques, can be carried out in Pennylane +by using `Mitiq `__, a Python library developed by Unitary Fund. + +ZNE in particular is also offered out of the box in Pennylane as a *differentiable* error mitigation technique, +for usage in combination with variational workflows. More on this in the tutorial +:doc:`Differentiating quantum error mitigation transforms `. + +On top of the error mitigation routines offered in Pennylane, ZNE is also available for just-in-time +(JIT) compilation. In this tutorial we see how an error mitigation routine can be +integrated in a Catalyst workflow. + +At the end of the tutorial, we will compare time for the execution of ZNE routines in +pure Pennylane vs. Pennylane Catalyst with JIT. + What is ZNE ----------- -Zero-noise extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum +ZNE is a technique used to mitigate the effect of noise on quantum computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum results by running circuits at varying noise levels and extrapolating back to a hypothetical zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail, let's first @@ -46,24 +64,6 @@ to estimate the zero-noise limit---the result we would expect in a noise-free scenario. Catalyst provides **polynomial** and **exponential** extrapolation methods. -Using ZNE with Pennylane ------------------------- - -The demo :doc:`Error mitigation with Mitiq and PennyLane ` -shows how ZNE, along with other error mitigation techniques, can be carried out in Pennylane by using `Mitiq `__, -a Python library developed by Unitary Fund. - -ZNE in particular is also offered out of the box in Pennylane as a *differentiable* error mitigation technique, -for usage in combination with variational workflows. More on this in the tutorial -:doc:`Differentiating quantum error mitigation transforms `. - -On top of the error mitigation routines offered in Pennylane, ZNE is also available for just-in-time -(JIT) compilation, starting from Catalyst v0.8.1. -In this tutorial we see how an error mitigation routine can be integrated in a Catalyst workflow. - -At the end of the tutorial, we will compare time for the execution of ZNE routines in -pure Pennylane vs. Pennylane Catalyst with JIT. - Defining the mirror circuit --------------------------- @@ -75,12 +75,11 @@ equal to 1. """ -import os import timeit -import pennylane as qml import numpy as np -from catalyst import qjit, mitigate_with_zne +import pennylane as qml +from catalyst import mitigate_with_zne n_wires = 5 @@ -162,7 +161,7 @@ def circuit(w1, w2): # to define a very simple :func:`~.QNode`, which represents the mitigated version of the original circuit. -@qjit +@qml.qjit def mitigated_circuit_qjit(w1, w2): return mitigate_with_zne( noisy_qnode, diff --git a/poetry.lock b/poetry.lock index aad693a7ff..d0b0c1b6fc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4011,43 +4011,31 @@ python-versions = ">=3.9" files = [ {file = "pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5"}, {file = "pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348"}, - {file = "pandas-2.2.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = 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+++ b/demonstrations/tutorial_zne_catalyst.metadata.json @@ -21,11 +21,11 @@ "previewImages": [ { "type": "thumbnail", - "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_qrack_catalyst_integration.png" + "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_qjit_compile_grovers_algorithm_with_catalyst.png" }, { "type": "large_thumbnail", - "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_qrack_catalyst_integration.png" + "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_qjit_compile_grovers_algorithm_with_catalyst.png" } ], "seoDescription": "Digital Zero-Noise Extrapolation with Catalyst", From 4e5c2211a37a13095c1e2db85edc2cc7a3295fc4 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Wed, 9 Oct 2024 17:03:43 +0200 Subject: [PATCH 33/44] Apply suggestions from Ivana's code review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Ivana Kurečić --- .../tutorial_zne_catalyst.metadata.json | 5 ++--- demonstrations/tutorial_zne_catalyst.py | 20 +++++++++---------- 2 files changed, 12 insertions(+), 13 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.metadata.json b/demonstrations/tutorial_zne_catalyst.metadata.json index f5d3ee6592..5ecc0b23f7 100644 --- a/demonstrations/tutorial_zne_catalyst.metadata.json +++ b/demonstrations/tutorial_zne_catalyst.metadata.json @@ -14,8 +14,7 @@ "dateOfLastModification": "2024-09-09T00:00:00+00:00", "categories": [ "Algorithms", - "Quantum Computing", - "How-to" + "Quantum Computing" ], "tags": [], "previewImages": [ @@ -28,7 +27,7 @@ "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_qjit_compile_grovers_algorithm_with_catalyst.png" } ], - "seoDescription": "Digital Zero-Noise Extrapolation with Catalyst", + "seoDescription": "Learn to use error mitigation and the digital zero-noise extrapolation (ZNE) technique with Catalyst, the PennyLane framework for quantum JIT compilation.", "doi": "", "canonicalURL": "/qml/demos/tutorial_zne_catalyst", "references": [ diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index eed12a7601..2582f83370 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -3,7 +3,7 @@ ============================================== In this tutorial, you will learn how to use error mitigation, and in particular -the Zero-noise extrapolation (ZNE) technique, in combination with +the zero-noise extrapolation (ZNE) technique, in combination with `Catalyst `_, a framework for quantum just-in-time (JIT) compilation with PennyLane. We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum @@ -15,7 +15,7 @@ The demo :doc:`Error mitigation with Mitiq and PennyLane ` shows how ZNE, along with other error mitigation techniques, can be carried out in Pennylane -by using `Mitiq `__, a Python library developed by Unitary Fund. +by using `Mitiq `__, a Python library developed by `Unitary Fund `__. ZNE in particular is also offered out of the box in Pennylane as a *differentiable* error mitigation technique, for usage in combination with variational workflows. More on this in the tutorial @@ -28,15 +28,15 @@ At the end of the tutorial, we will compare time for the execution of ZNE routines in pure Pennylane vs. Pennylane Catalyst with JIT. -What is ZNE +What is zero-noise extrapolation (ZNE) ----------- -ZNE is a technique used to mitigate the effect of noise on quantum +Zero-noise extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum results by running circuits at varying noise levels and extrapolating back to a hypothetical zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail, let's first review what happens when using the protocol in practice. -Stage 1: Generating Noise-Scaled Circuits +Stage 1: Generating noise-scaled circuits ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In its digital version [#DZNEpaper]_, ZNE works by generating circuits with **increased** noise. @@ -68,7 +68,7 @@ --------------------------- The first step for demoing an error mitigation routine is to define a circuit. -Here we build a simple mirror-circuit starting off a unitary 2-design. +Here we build a simple mirror circuit starting off a unitary 2-design. This is a typical construction for a randomized benchmarking circuit, which is used in many tasks in quantum computing. Given such circuit, we measure the expectation value :math:`\langle Z\rangle` on the state of the first qubit, and by construction of the circuit, we expect this value to be @@ -129,8 +129,8 @@ def circuit(w1, w2): # missing some necessary parameters. In particular we still need to specify: # # 1. The method for scaling this noise up (in Catalyst there are two options: ``global`` and -# ``local``) -# 2. The noise scaling factors (i.e. how much to increase the depth of the circuit) +# ``local``). +# 2. The noise scaling factors (i.e. how much to increase the depth of the circuit). # 3. The extrapolation technique used to estimate the ideal value (available in Catalyst are # polynomial and exponential extrapolation). # @@ -149,7 +149,7 @@ def circuit(w1, w2): # Finally, we'll choose the extrapolation technique. Both exponential and polynomial extrapolation # is available in the :mod:`qml.transforms ` module, and both of these functions can be passed directly # into Catalyst's :func:`catalyst.mitigate_with_zne` function. In this tutorial we use polynomial extrapolation, -# which we hypothesize it best models the behavior of the noise scenario we are considering. +# which we hypothesize best models the behavior of the noise scenario we are considering. from pennylane.transforms import poly_extrapolate from functools import partial @@ -236,7 +236,7 @@ def mitigated_circuit(w1, w2): # - **ZNE extrapolation** # - **Differentiable** # - **JIT** -# - **other QEM techniques** +# - **Other QEM techniques** # * - Pennylane + Mitiq # - global, local, random # - polynomial, exponential From 7db63734c8b39be0fe4704eb47ca74b6885b7bc2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ivana=20Kure=C4=8Di=C4=87?= Date: Wed, 9 Oct 2024 17:54:07 +0200 Subject: [PATCH 34/44] correct thumbnails --- ...compile_grovers_algorithm_with_catalyst.png | Bin 42737 -> 0 bytes .../thumbnail_large_zne_catalyst.png | Bin 0 -> 119937 bytes ...compile_grovers_algorithm_with_catalyst.png | Bin 40938 -> 0 bytes .../OGthumbnail_zne_catalyst.png | Bin 0 -> 113615 bytes ...compile_grovers_algorithm_with_catalyst.png | Bin 23987 -> 0 bytes .../thumbnail_zne_catalyst.png | Bin 0 -> 27724 bytes .../tutorial_zne_catalyst.metadata.json | 4 ++-- 7 files changed, 2 insertions(+), 2 deletions(-) delete mode 100644 _static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_qjit_compile_grovers_algorithm_with_catalyst.png create mode 100644 _static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_zne_catalyst.png delete mode 100644 _static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_qjit_compile_grovers_algorithm_with_catalyst.png create mode 100644 _static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_zne_catalyst.png delete mode 100644 _static/demo_thumbnails/regular_demo_thumbnails/thumbnail_qjit_compile_grovers_algorithm_with_catalyst.png create mode 100644 _static/demo_thumbnails/regular_demo_thumbnails/thumbnail_zne_catalyst.png diff --git 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zPT;O@*6)+Q#P@*i@Y2$M7cim!HmNg{lT7)se)VfP`H0%)=_guZ$$BjFr)DK(tK%XX- zQqeF#4Pe8*sV>#9`5k`3H1$apFUIVXj-f6rbu1>ym+c*n)UAqGB)cA6w5SD{$Om@C z?{Hm*9xoT4sDzUq(m&u@zi0B@;0|_r#%+1RvSVAdNVQFtKiJS_yJbT2bI-VS*K8g(&C}EGOm2ApnKr*NVflCa99eG& zm{B8&n&pX}hxG<^+W@*y_x=<~X4@v3JBL*5hMhTRkbW6@5(jdIKR6*^#@D zHDYPZ!@*LAROku*&Z7@YfoR&kh`}MsAGD4|@)2zDJ-tIbO>We&v}viAH~a~ZNS)?6 z22tNk|4IMSKX9SwZ7ZbYSwhT$1F` -shows how ZNE, along with other error mitigation techniques, can be carried out in Pennylane +shows how ZNE, along with other error mitigation techniques, can be carried out in PennyLane by using `Mitiq `__, a Python library developed by `Unitary Fund `__. -ZNE in particular is also offered out of the box in Pennylane as a *differentiable* error mitigation technique, +ZNE in particular is also offered out of the box in PennyLane as a *differentiable* error mitigation technique, for usage in combination with variational workflows. More on this in the tutorial :doc:`Differentiating quantum error mitigation transforms `. -On top of the error mitigation routines offered in Pennylane, ZNE is also available for just-in-time +On top of the error mitigation routines offered in PennyLane, ZNE is also available for just-in-time (JIT) compilation. In this tutorial we see how an error mitigation routine can be integrated in a Catalyst workflow. At the end of the tutorial, we will compare time for the execution of ZNE routines in -pure Pennylane vs. Pennylane Catalyst with JIT. +pure PennyLane vs. PennyLane Catalyst with JIT. What is zero-noise extrapolation (ZNE) ----------- @@ -113,8 +113,7 @@ def circuit(w1, w2): # ---------------------------- # Let's now run the circuit through a noisy scenario. The Qrack simulator models noise by # applying single-qubit depolarizing noise channels to all qubits in all gates of the circuit. -# The probability of error is specified by the value of the `noise` constructor argument (or -# the `QRACK_GATE_DEPOLARIZATION` environment variable). +# The probability of error is specified by the value of the ``noise`` constructor argument. NOISE_LEVEL = 0.01 noisy_device = qml.device("qrack.simulator", n_wires, noise=NOISE_LEVEL) @@ -200,7 +199,7 @@ def mitigated_circuit(w1, w2): zne_value = mitigated_circuit(w1, w2) -print(f"Error with ZNE in Pennylane: {abs(ideal_value - zne_value):.3f}") +print(f"Error with ZNE in PennyLane: {abs(ideal_value - zne_value):.3f}") ############################################################################## # To showcase the impact of JIT compilation, let's use Python's ``timeit`` module @@ -223,9 +222,9 @@ def mitigated_circuit(w1, w2): # Already with the simple circuit we started with, and with the simple parameters in our example, # we can appreciate the performance differences. That was at the cost of very minimal syntax change. # -# There are still reasons to use ZNE in Pennylane without :func:`~.qjit`, for instance, +# There are still reasons to use ZNE in PennyLane without :func:`~.qjit`, for instance, # whenever the device of choice is not supported by Catalyst. To help, -# we conlcude with a landscape of the QEM techniques available on Pennylane. +# we conlcude with a landscape of the QEM techniques available on PennyLane. # # .. list-table:: # :widths: 30 20 20 20 20 30 @@ -237,13 +236,13 @@ def mitigated_circuit(w1, w2): # - **Differentiable** # - **JIT** # - **Other QEM techniques** -# * - Pennylane + Mitiq +# * - PennyLane + Mitiq # - global, local, random # - polynomial, exponential # - # - # - PEC, CDR, DDD, REM -# * - Pennylane transforms +# * - PennyLane transforms # - global, local # - polynomial, exponential # - ✅ From 65cb5a2d763cf2879b059d6fd3bb0bf679b96436 Mon Sep 17 00:00:00 2001 From: nate stemen Date: Wed, 9 Oct 2024 12:26:51 -0700 Subject: [PATCH 36/44] migrate info to pennylane.ai --- _static/authors/nate_stemen.png | Bin 2457915 -> 0 bytes _static/authors/nate_stemen.txt | 4 ---- .../tutorial_zne_catalyst.metadata.json | 3 +-- 3 files changed, 1 insertion(+), 6 deletions(-) delete mode 100644 _static/authors/nate_stemen.png 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He earned a master’s degree from the University of Waterloo, where he focused on quantum circuit compilation. He is passionate about advancing effective education in quantum computing. \ No newline at end of file diff --git a/demonstrations/tutorial_zne_catalyst.metadata.json b/demonstrations/tutorial_zne_catalyst.metadata.json index f834d3508d..b043bb7e09 100644 --- a/demonstrations/tutorial_zne_catalyst.metadata.json +++ b/demonstrations/tutorial_zne_catalyst.metadata.json @@ -5,8 +5,7 @@ "username": "cosenal" }, { - "id": "nate_stemen", - "affiliation": "Unitary Fund" + "username": "natestemen" } ], "dateOfPublication": "2024-09-09T00:00:00+00:00", From da89e5d7fdccce90d79970c2b8c7525ac7a615e6 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 10 Oct 2024 15:08:16 +0200 Subject: [PATCH 37/44] Apply suggestions from code review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Ivana Kurečić --- demonstrations/tutorial_zne_catalyst.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 8f6237c4e4..9d46f09de7 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -25,8 +25,8 @@ (JIT) compilation. In this tutorial we see how an error mitigation routine can be integrated in a Catalyst workflow. -At the end of the tutorial, we will compare time for the execution of ZNE routines in -pure PennyLane vs. PennyLane Catalyst with JIT. +At the end of the tutorial, we will compare the execution time of ZNE routines in +pure PennyLane vs. PennyLane and Catalyst with JIT. What is zero-noise extrapolation (ZNE) ----------- @@ -224,7 +224,7 @@ def mitigated_circuit(w1, w2): # # There are still reasons to use ZNE in PennyLane without :func:`~.qjit`, for instance, # whenever the device of choice is not supported by Catalyst. To help, -# we conlcude with a landscape of the QEM techniques available on PennyLane. +# we conclude with a landscape of the QEM techniques available on PennyLane. # # .. list-table:: # :widths: 30 20 20 20 20 30 From 2cb117688a94bf49e5f6491dcb6bb18e486d43be Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 10 Oct 2024 15:21:42 +0200 Subject: [PATCH 38/44] clarify some sentences --- demonstrations/tutorial_zne_catalyst.py | 20 +++++++------------- 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 9d46f09de7..5ea54d5453 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -33,14 +33,16 @@ Zero-noise extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum results by running circuits at varying noise levels and extrapolating back to a hypothetical -zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail, let's first -review what happens when using the protocol in practice. +zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail (for which we +recommend reading the `Mitiq docs`_ +and the references therein), let's first review what happens when using the protocol in practice. Stage 1: Generating noise-scaled circuits ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In its digital version [#DZNEpaper]_, ZNE works by generating circuits with **increased** noise. -Currently, ZNE in Catalyst supports two methods for generating noise-scaled circuits: +ZNE works by generating circuits with **increased** noise. Catalyst implements the unitary folding +framework introduced in [#DZNEpaper]_ for generating noise-scaled circuits. In particular, +the following two methods are available: 1. **Global folding**: If a circuit implements a global unitary :math:`U`, global folding applies :math:`U(U^\dagger U)^n` for some integer :math:`n`, @@ -106,7 +108,7 @@ def circuit(w1, w2): print(f"Ideal value: {ideal_value}") ############################################################################## -# As expected, in the noiseless scenario, the expecation value of the Pauli-Z measurement +# In the noiseless scenario, the expectation value of the Pauli-Z measurement # is equal to 1, since the first qubit is back in the :math:`|0\rangle` state. # # Mitigating the noisy circuit @@ -269,11 +271,3 @@ def mitigated_circuit(w1, w2): # Tudor Giurgica-Tiron, Yousef Hindy, Ryan LaRose, Andrea Mari, and William J. Zeng # "Digital zero noise extrapolation for quantum error mitigation" # `arXiv:2005.10921v2 `__, 2020. -# - -############################################################################## -# About the authors -# ----------------- -# .. include:: ../_static/authors/alessandro_cosentino.txt -# -# .. include:: ../_static/authors/nate_stemen.txt From bc166cff32447e039fcc305a12aad611a4296762 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino Date: Thu, 10 Oct 2024 15:39:17 +0200 Subject: [PATCH 39/44] addressing more review comments --- demonstrations/tutorial_zne_catalyst.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index 5ea54d5453..fd58dccd87 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -10,8 +10,9 @@ simulator, and use extrapolation techniques to estimate the zero-noise result, all while leveraging JIT compilation through Catalyst. -Using ZNE with PennyLane ------------------------- +.. image:: ../_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_zne_catalyst.png + :width: 65% + :align: center The demo :doc:`Error mitigation with Mitiq and PennyLane ` shows how ZNE, along with other error mitigation techniques, can be carried out in PennyLane @@ -34,7 +35,7 @@ computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum results by running circuits at varying noise levels and extrapolating back to a hypothetical zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail (for which we -recommend reading the `Mitiq docs`_ +recommend reading the `Mitiq docs `_ and the references therein), let's first review what happens when using the protocol in practice. Stage 1: Generating noise-scaled circuits From 17735ba9b0a4cca59405c2a239ca9c849d09fb77 Mon Sep 17 00:00:00 2001 From: WrathfulSpatula Date: Mon, 28 Oct 2024 17:58:03 -0400 Subject: [PATCH 40/44] one-shot mode for ZNE tutorial noisy device --- demonstrations/tutorial_zne_catalyst.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py index fd58dccd87..673eb0bfac 100644 --- a/demonstrations/tutorial_zne_catalyst.py +++ b/demonstrations/tutorial_zne_catalyst.py @@ -119,9 +119,9 @@ def circuit(w1, w2): # The probability of error is specified by the value of the ``noise`` constructor argument. 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a/pyproject.toml b/pyproject.toml index 9ab02da137..9bed5d9256 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,8 +65,8 @@ optax = "0.2.3" flax = "0.9.0" qutip = "4.7.3" mitiq = "0.32.0" -pennylane-qrack = "0.10.0" -pyqrack = "1.31.0" +pennylane-qrack = "0.10.6" +pyqrack = "1.32.12" zstd = "*" dill = "*" stim = "*" From 02f9541d15dbb0ada4d79d3c1957c8285436837b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ivana=20Kure=C4=8Di=C4=87?= Date: Thu, 7 Nov 2024 16:44:32 +0100 Subject: [PATCH 44/44] poetry --- poetry.lock | 40 +++++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/poetry.lock b/poetry.lock index e6bb968ab1..1a93345c53 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4276,19 +4276,20 @@ sympy = "<1.13" [[package]] name = "pennylane-qrack" -version = "0.10.0" +version = "0.10.6" description = "PennyLane plugin for Qrack." optional = false python-versions = "*" files = [ - {file = 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b/demonstrations/tutorial_zne_catalyst.metadata.json index 5ecc0b23f7..9a2160cd07 100644 --- a/demonstrations/tutorial_zne_catalyst.metadata.json +++ b/demonstrations/tutorial_zne_catalyst.metadata.json @@ -20,11 +20,11 @@ "previewImages": [ { "type": "thumbnail", - "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_qjit_compile_grovers_algorithm_with_catalyst.png" + "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_zne_catalyst.png" }, { "type": "large_thumbnail", - "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_qjit_compile_grovers_algorithm_with_catalyst.png" + "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_zne_catalyst.png" } ], "seoDescription": "Learn to use error mitigation and the digital zero-noise extrapolation (ZNE) technique with Catalyst, the PennyLane framework for quantum JIT compilation.", From aa2e265ff89525c28236097698794bec4e037d03 Mon Sep 17 00:00:00 2001 From: Alessandro Cosentino 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