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pyGSTi 0.9.12.1

master build develop build beta build notebooks on beta

pyGSTi

pyGSTi is an open-source software for modeling and characterizing noisy quantum information processors (QIPs), i.e., systems of one or more qubits. It is licensed under the Apache License, Version 2.0. Copyright information can be found in NOTICE, and the license itself in LICENSE.

There are three main objects in pyGSTi:

  • Circuit: a quantum circuit (can have many qubits).
  • Model: a description of a QIP's gate and SPAM operations (a noise model).
  • DataSet: a dictionary-like container holding experimental data.

You can do various things by with these objects:

  • Circuit simulation: compute a the outcome probabilities of a Circuit using a Model.
  • Data simulation: simulate experimental data (a DataSet) using a Model.
  • Model testing: Test whether a given Model fits the data in a DataSet.
  • Model estimation: Estimate a Model from a DataSet (e.g. using GST).
  • Model-less characterization: Perform Randomized Benchmarking on a DataSet.

In particular, there are a number of characterization protocols currently implemented in pyGSTi:

  • Gate Set Tomography (GST) is the most complex and is where the software derives its name (a "python GST implementation").
  • Randomized Benchmarking (RB) is a well-known method for assessing the quality of a QIP in an average sense. PyGSTi implements standard "Clifford" RB as well as the more scalable "Direct" RB methods.
  • Robust Phase Estimation (RPE) is a method designed for quickly learning a few noise parameters of a QIP that particularly useful for tuning up qubits.

PyGSTi is designed with a modular structure so as to be highly customizable and easily integrated to new or existing python software. It runs using python 3.8 or higher. To faclilitate integration with software for running cloud-QIP experiments, pyGSTi Circuit objects can be converted to IBM's OpenQASM and Rigetti Quantum Computing's Quil circuit description languages.

Installation

Apart from several optional Cython modules, pyGSTi is written entirely in Python. To install pyGSTi and only its required dependencies run:

pip install pygsti

Or, to install pyGSTi with all its optional dependencies too, run:

pip install pygsti[complete]

The disadvantage to these approaches is that the numerous tutorials included in the package will then be buried within your Python's site_packages directory, which you'll likely want to access later on. Alternatively, you can locally install pyGSTi using the following commands:

cd <install_directory>
git clone https://github.com/pyGSTio/pyGSTi.git
cd pyGSTi
pip install -e .[complete]

As above, you can leave off the .[complete] if you only went the minimal set of dependencies installed. You could also replace the git clone ... command with unzip pygsti-0.9.x.zip where the latter file is a downloaded pyGSTi source archive. Any of the above installations should build the set of optional Cython extension modules if a working C/C++ compiler and the Cython package are present. If, however, compilation fails or you later decided to add Cython support, you can rebuild the extension modules (without reinstalling) if you've followed the local installation approach above using the command:

python setup.py build_ext --inplace

Finally, Jupyter notebook is highly recommended as it is generally convenient and the format of the included tutorials and examples. It is installed automatically when [complete] is used, otherwise it can be installed separately.

Getting Started

Here's a couple of simple examples to get you started.

Circuit simulation

To compute the outcome probabilities of a circuit, you just need to create a Circuit object (describing your circuit) and a Model object containing the operations contained in your circuit. Here we use a "stock" single-qubit Model containing Idle, X(π/2), and Y(π/2) gates labelled Gi, Gx, and Gy, respectively:

import pygsti
from pygsti.modelpacks import smq1Q_XYI

mycircuit = pygsti.circuits.Circuit([('Gxpi2',0), ('Gypi2',0), ('Gxpi2',0)])
model = smq1Q_XYI.target_model()
outcome_probabilities = model.probabilities(mycircuit)

Gate Set Tomography

Gate Set Tomography is used to characterize the operations performed by hardware designed to implement a (small) system of quantum bits (qubits). Here's the basic idea:

  1. you tell pyGSTi what gates you'd ideally like to perform

  2. pyGSTi tells you what circuits it want's data for

  3. you perform the requested experiments and place the resulting data (outcome counts) into a text file that looks something like:

    ## Columns = 0 count, 1 count
{} 0 100  # the empty sequence (just prep then measure)
Gx 10 90  # prep, do a X(pi/2) gate, then measure
GxGy 40 60  # prep, do a X(pi/2) gate followed by a Y(pi/2), then measure
Gx^4 20 80  # etc...

  4. pyGSTi takes the data file and outputs a "report" - currently a HTML web page.

In code, running GST looks something like this:

import pygsti
from pygsti.modelpacks import smq1Q_XYI

# 1) get the ideal "target" Model (a "stock" model in this case)
mdl_ideal = smq1Q_XYI.target_model()

# 2) generate a GST experiment design
edesign = smq1Q_XYI.create_gst_experiment_design(4) # user-defined: how long do you want the longest circuits?

# 3) write a data-set template
pygsti.io.write_empty_dataset("MyData.txt", edesign.all_circuits_needing_data, "## Columns = 0 count, 1 count")

# STOP! "MyData.txt" now has columns of zeros where actual data should go.
# REPLACE THE ZEROS WITH ACTUAL DATA, then proceed with:
ds = pygsti.io.load_dataset("MyData.txt") # load data -> DataSet object

# OR: Create a simulated dataset with:
# ds = pygsti.data.simulate_data(mdl_ideal, edesign, num_samples=1000)

# 4) run GST (now using the modern object-based interface)
data = pygsti.protocols.ProtocolData(edesign, ds) # Step 1: Bundle up the dataset and circuits into a ProtocolData object
protocol = pygsti.protocols.StandardGST() # Step 2: Select a Protocol to run
results = protocol.run(data) # Step 3: Run the protocol!

# 5) Create a nice HTML report detailing the results
report = pygsti.report.construct_standard_report(results, title="My Report", verbosity=1)
report.write_html("myReport", auto_open=True, verbosity=1) # Can also write out Jupyter notebooks!

Tutorials and Examples

There are numerous tutorials (meant to be pedagogical) and examples (meant to be demonstrate how to do some particular thing) in the form of Jupyter notebooks beneath the pyGSTi/jupyter_notebooks directory. The root "START HERE" notebook will direct you where to go based on what you're most interested in learning about. You can view the read-only GitHub version of this notebook or you can explore the tutorials interactively using JupyterHub via Binder. Note the existence of a FAQ, which addresses common issues.

Running notebooks locally

While it's possible to view the notebooks on GitHub using the links above, it's usually nicer to run them locally so you can mess around with the code as you step through it. To do this, you'll need to start up a Jupyter notebook server using the following steps (this assumes you've followed the local installation directions above):

  • Changing to the notebook directory, by running: cd jupyter_notebooks/Tutorials/

  • Start up the Jupyter notebook server by running: jupyter notebook

The Jupyter server should open up your web browser to the server root, from where you can start the first "START_HERE.ipynb" notebook. Note that the key command to execute a cell within the Jupyter notebook is Shift+Enter, not just Enter.

Documentation

Online documentation is hosted on Read the Docs.

License

PyGSTi is licensed under the Apache License Version 2.0.

Questions?

For help and support with pyGSTi, please contact the authors at [email protected].