PyQuil is a Python library for quantum programming using Quil, the quantum instruction language developed at Rigetti Computing. PyQuil serves three main functions:
- Easily generating Quil programs from quantum gates and classical operations
- Compiling and simulating Quil programs using the Quil Compiler (quilc) and the Quantum Virtual Machine (QVM)
- Executing Quil programs on real quantum processors (QPUs) using Quantum Cloud Services (QCS)
PyQuil has a ton of other features, which you can learn more about in the docs. However, you can also keep reading below to get started with running your first quantum program!
Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter notebook tutorials and examples. To run them in a preconfigured execution environment on Binder, click the "launch binder" badge at the top of the README or the link here! To learn more about the tutorials and how you can add your own, visit the rigetti/forest-tutorials repository. If you'd rather set everything up locally, or are interested in contributing to pyQuil, continue onto the next section for instructions on installing pyQuil and the Forest SDK.
PyQuil can be installed using conda, pip, or from source. To install it from PyPI (via pip),
do the following:
pip install pyquilTo instead install pyQuil from source, do the following from within the repository after cloning it:
pip install -e .If you choose to use pip, we highly recommend installing pyQuil within a virtual environment.
PyQuil, along with quilc, the QVM, and other libraries, make up what is called the Forest SDK. To make full use of pyQuil, you will need to additionally have installed quilc and the QVM. For more information, check out the docs!
In just a few lines, we can use pyQuil with the Forest SDK to simulate a Bell state!
from pyquil import get_qc, Program
from pyquil.gates import CNOT, H, MEASURE
qvm = get_qc('2q-qvm')
p = Program()
p += H(0)
p += CNOT(0, 1)
ro = p.declare('ro', 'BIT', 2)
p += MEASURE(0, ro[0])
p += MEASURE(1, ro[1])
p.wrap_in_numshots_loop(10)
qvm.run(p).get_register_map()['ro'].tolist()The output of the above program should look something like the following, the statistics of which are consistent with a two-qubit entangled state.
[[0, 0],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[0, 0],
[0, 0],
[1, 1],
[0, 0],
[0, 0]]
Using the Forest SDK, you can simulate the operation of a real quantum processor (QPU). If you would like to run on the real QPUs in our lab in Berkeley, you can sign up for an account on Quantum Cloud Services (QCS)!
If you'd like to get involved with pyQuil and Forest, joining the Rigetti Forest Slack Workspace is a great place to start! You can do so by clicking the invite link in the previous sentence, or in the badge at the top of this README. The Slack Workspace is a great place to ask general questions, join high-level design discussions, and hear about updates to pyQuil and the Forest SDK.
To go a step further and start contributing to the development of pyQuil, good first steps are reporting a bug, requesting a feature, or picking up one of the issues with the good first issue or help wanted labels. Once you find an issue to work on, make sure to fork this repository and then open a pull request once your changes are ready. For more information on all the ways you can contribute to pyQuil (along with some helpful tips for developers and maintainers) check out our Contributing Guide!
To see what people have contributed in the past, check out the Changelog for a detailed list of all announcements, improvements, changes, and bugfixes. The Releases page for pyQuil contains similar information, but with links to the pull request for each change and its corresponding author. Thanks for contributing to pyQuil! 🙂
If you use pyQuil, Grove, or other parts of the Forest SDK in your research, please cite the Quil specification using the following BibTeX snippet:
@misc{smith2016practical,
title={A Practical Quantum Instruction Set Architecture},
author={Robert S. Smith and Michael J. Curtis and William J. Zeng},
year={2016},
eprint={1608.03355},
archivePrefix={arXiv},
primaryClass={quant-ph}
}Additionally, if your research involves taking data on Rigetti quantum processors (QPUs) via the Quantum Cloud Services (QCS) platform, please reference the QCS paper using the following BibTeX snippet:
@article{Karalekas_2020,
title = {A quantum-classical cloud platform optimized for variational hybrid algorithms},
author = {Peter J Karalekas and Nikolas A Tezak and Eric C Peterson
and Colm A Ryan and Marcus P da Silva and Robert S Smith},
year = 2020,
month = {apr},
publisher = {{IOP} Publishing},
journal = {Quantum Science and Technology},
volume = {5},
number = {2},
pages = {024003},
doi = {10.1088/2058-9565/ab7559},
url = {https://doi.org/10.1088%2F2058-9565%2Fab7559},
}The preprint of the QCS paper is available on arXiv, and the supplementary interactive notebooks and datasets for the paper can be found in the rigetti/qcs-paper repository.
PyQuil is licensed under the Apache License 2.0.
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