qLDPC

This package contains tools for constructing and analyzing quantum low density parity check (qLDPC) codes. At least, that was the original motivation for this package. In practice, the tools here can work just as well for more general error-correcting stabilizer and subsystem codes.

📦 Installation

This package requires Python>=3.10, and can be installed from the Python Package Index (PyPI) with

pip install qldpc

To install a local version of qLDPC from source:

git clone https://github.com/Infleqtion/qLDPC.git
pip install -e qLDPC

You can also pip install -e 'qLDPC[dev]' to additionally install some development tools.

Windows

If you use Windows you will need to have a C compiler installed, as discussed here, before to installing qLDPC. One option is to install Visual Studio (2022 or later).

macOS

If you use macOS you may need to install cvxpy manually by following the instructions here before installing qLDPC. If you use conda to manage your python environment, you can obtain cvxpy simply by running conda install -c conda-forge cvxpy.

🚀 Features

Notable features include:

🤔 Questions and issues

This project aspires to have a documentation page, but at the moment the documentation is out of date and auto-generated from source code that was written to be human-readable in a plain text editor. For now, I recommend looking at the source code (and comments therein) directly, as well as the examples directory. Test files (such as qldpc/codes/quantum_test.py) also contain some examples of using the classes and methods in this library.

If you have any questions, feedback, or requests, please open an issue on GitHub or email me at mika.perlin@gmail.com!

⚓ Attribution

If you use this software in your work, please cite with:

@misc{perlin2023qldpc,
  author = {Perlin, Michael A.},
  title = {{qLDPC}},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Infleqtion/qLDPC}},
}

This may require adding \usepackage{url} to your LaTeX file header. Alternatively, you can cite

Michael A. Perlin. qLDPC. https://github.com/Infleqtion/qLDPC, 2023.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.3)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for perlinm from gravatar.com perlinm

Credits

Author: Michael A. Perlin

License

Apache Software License (Apache-2.0)

Requires

Python <4.0, >=3.10

Tags

quantum computing quantum error correction low density partiy check codes LDPC

Classifiers

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