Python package for fault-tolerant quantum algorithms research.

Installation – Usage – Documentation – Community – Citation – Contact

Qualtran is a set of abstractions for representing quantum programs and a library of quantum algorithms expressed in that language to support quantum algorithms research.

Installation

Qualtran is being actively developed. We recommend installing from the source code.

The following commands will clone a copy of the repository, then install the Qualtran package in your local Python environment as a local editable copy:

git clone https://github.com/quantumlib/Qualtran.git
cd Qualtran/
pip install -e .

You can also install the latest tagged release using pip:

pip install qualtran

You can also install the latest version of the main branch on GitHub:

pip install git+https://github.com/quantumlib/Qualtran

Usage

Warning

Qualtran is an experimental preview release. We provide no backwards compatibility guarantees. Some algorithms or library functionality may be incomplete or contain inaccuracies. Open issues or contact the authors with bug reports or feedback.

You should be able to import the qualtran package into your interactive Python environment as as well as your programs:

import qualtran

If this is successful, you can move on to learning how to write bloqs or investigate the bloqs library.

Documentation

Documentation is available at https://qualtran.readthedocs.io/.

Community

Qualtran's community is growing rapidly, and if you'd like to join the many open-source contributors to the Qualtran project, we welcome your participation! We are dedicated to cultivating an open and inclusive community, and have a code of conduct.

Announcements

You can stay on top of Qualtran news using the approach that best suits your needs:

• For releases and major announcements: join the low-volume mailing list qualtran-announce.
• For releases only:
  • Via GitHub notifications: configure repository notifications for Qualtran.
  • Via RSS from GitHub: subscribe to the GitHub Qualtran releases feed.
  • Via RSS from PyPI: subscribe to the PyPI releases feed for Qualtran.

Questions and Discussions

• If you'd like to ask questions and participate in discussions, join the qualtran-dev group/mailing list. By joining qualtran-dev, you will also get automated invites to the biweekly Qualtran Sync meeting (below).

• Would you like to get more involved in Qualtran development? The biweekly Qualtran Sync is a virtual face-to-face meeting of contributors to discuss everything from issues to ongoing efforts, as well as to ask questions. Become a member of qualtran-dev to get an automatic meeting invitation!

Issues and Pull Requests

• Do you have a feature request or want to report a bug? Open an issue on GitHub to report it!
• Do you have a code contribution? Read our contribution guidelines, then open a pull request!

Citation

When publishing articles or otherwise writing about Qualtran, please cite the following:

@misc{harrigan2024qualtran,
    title={Expressing and Analyzing Quantum Algorithms with Qualtran},
    author={Matthew P. Harrigan and Tanuj Khattar
        and Charles Yuan and Anurudh Peduri and Noureldin Yosri
        and Fionn D. Malone and Ryan Babbush and Nicholas C. Rubin},
    year={2024},
    eprint={2409.04643},
    archivePrefix={arXiv},
    primaryClass={quant-ph},
    doi={10.48550/arXiv.2409.04643},
    url={https://arxiv.org/abs/2409.04643},
}

Contact

For any questions or concerns not addressed here, please email [email protected].

Disclaimer

This is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.

Copyright 2025 Google LLC.