pyGSTi 0.10

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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 3.10 or higher. To facilitate 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/sandialabs/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!

Documentation

There are numerous tutorials (meant to be pedagogical) and examples (meant to be demonstrate how to do some particular thing) in the pyGSTi/docs directory. These are stored as MyST Markdown for version control convenience, but can be converted to Jupyter notebooks as needed using Jupytext.

Viewing the documentation online

The recommended way to view the documentation is on ReadTheDocs, although the raw Markdown files can also be looked at on GitHub.

The site renders the source MyST Markdown without executing notebook cells, so you won't see outputs (plots, tables) inline. Each rendered page offers two ways to run the notebook yourself:

  • Rocket icon → Binder or Colab: launches a fully-provisioned notebook environment in your browser with no local install.
  • Cloud/download icon → "Download this page" dropdown: grab the .ipynb (or the .md source) and run it in your own Jupyter setup.

Building the documentation locally

The docs are built using Jupyter Book v1. Note: v2 is a separate product (MyST Engine + Node.js) that doesn't support our autodoc-based API reference yet, so we pin jupyter-book<2.

To install the build dependencies along with pyGSTi: pip install -e .[docs]

then build: jb build docs

Then open docs/_build/html/index.html in a web browser to look through the documentation.

Running notebooks locally

It can also be convenient to build and run the tutorials locally. We can do this using Jupytext for conversion and then start a Jupyter notebook or JupyterLab server to run the notebooks. Assuming you've followed the local installation directions above:

  • Change to the docs directory, by running: cd docs

  • Build the notebooks from their Markdown sources, by running: jupytext --to notebook markdown/**/*.md This writes an .ipynb next to each .md under markdown/.

  • Start up the Jupyter notebook server by running jupyter notebook or a JupyterLab server by running jupyter lab.

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

Contributing notebook changes

Only the docs/markdown/*.md files are version-controlled. The paired .ipynb files — generated next to each .md under docs/markdown/ — are gitignored build artifacts. The canonical source is the .md file, and edits there "win" on the next sync. So:

  • If you find it more convenient to edit the .ipynb (e.g. for interactive iteration), sync your edits back to Markdown before committing: from the docs/ folder run jupytext --sync markdown/**/*.md, then commit the updated .md.
  • If you edit a .ipynb and forget to sync, your changes will be silently overwritten the next time anyone runs the sync. When in doubt, edit the .md.

A few other gotchas worth knowing when editing the MyST sources:

  • Block math ($$...$$) needs blank lines around it. A single $ is inline math; multiple $$ blocks crammed against surrounding text get parsed as inline math chains and render incorrectly.
  • Don't change kernelspec.name away from python3 when adding a new file. Jupyter writes the local conda env name into the notebook metadata; that name then propagates through jupytext --sync and breaks execution on anyone else's machine.
  • Adding a new tutorial requires editing docs/_toc.yml to slot the new file into the navigation.

License

PyGSTi is licensed under the Apache License Version 2.0.

Questions?

For help and support with pyGSTi, please contact the authors at pygsti@sandia.gov.

How To Cite pyGSTi

If you've used pyGSTi in the your research and are interested in citing us, please consider the following software design paper from some of the members of our development team (bibtex below):

@ARTICLE{Nielsen2020-rd,
  title     = "Probing quantum processor performance with {py{GST}i}",
  author    = "Nielsen, Erik and Rudinger, Kenneth and Proctor, Timothy and
              Russo, Antonio and Young, Kevin and Blume-Kohout, Robin",
  journal   = "Quantum Sci. Technol.",
  publisher = "IOP Publishing",
  volume    =  5,
  number    =  4,
  pages     = "044002",
  month     =  jul,
  year      =  2020,
  url       = "https://iopscience.iop.org/article/10.1088/2058-9565/ab8aa4",
  copyright = "http://iopscience.iop.org/page/copyright",
  issn      = "2058-9565",
  doi       = "10.1088/2058-9565/ab8aa4"
}

Project links

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Credits

Author: Erik Nielsen, Stefan Seritan, Corey Ostrove, Riley Murray, Jordan Hines, Kenneth Rudinger, Timothy Proctor, John Gamble, Robin Blume-Kohout

GitHub Statistics

Data verified by PyPI on Jul 30, 2026
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Python >=3.10

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diamond-norm docs evolutionary-optimization extensions html-reports ibmq interpygate linting memory-profiling mongodb msgpack multiprocessor notebooks pytorch report-pdf-figures reports testing-no-cython-mpi testing-no-cython testing no-mpi complete

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