Skip to main content

Optimize and simulate measurement-based quantum computation

Project description

logo

Documentation Status GitHub PyPI - Python Version PyPI Unitary Fund DOI

Graphix is a measurement-based quantum computing (MBQC) compiler, which makes it easier to generate, optimize and simulate MBQC measurement patterns.

Feature

  • We integrate an efficient graph state simulator as an optimization routine of MBQC measurement pattern, with which we can classically preprocess all Pauli measurements (corresponding to the elimination of all Clifford gates in the gate network - c.f. Gottesman-Knill theorem), significantly reducing the required size of graph state to run the computation.
  • We implement tensor-network simulation of MBQC with which thousands of qubits (graph nodes) can be simulated with modest computing resources (e.g. laptop), without approximation.
  • Our pattern-based construction and optimization routines are suitable for high-level optimization to run quantum algorithms on MBQC quantum hardware with minimal resource state size requirements. We plan to add quantum hardware emulators (and quantum hardware) as pattern execution backends.

Installation

Install graphix with pip:

$ pip install graphix

Next Steps

  • We have a few demos showing basic usages of Graphix.

  • You can run demos on your browser:

    • Preprocessing Clifford gates: Binder
    • Using tensor-network simulator backend: Binder
    • QAOA circuit: Binder
  • Read the tutorial for more comprehensive guide.

  • For theoretical background, read our quick introduction into MBQC and LC-MBQC.

Citing

S. Sunami and M. Fukushima. "Graphix: optimizing and simulating measurement-based quantum computation on local-Clifford decorated graph", arXiv:2212.11975 (2022).

Update on the paper: [^1]

[^1]: Following the release of this arXiv preprint, we were made aware of a previous work by Backens et al. where Pauli measurement elimination method for MBQC was developed in the context of circuit optimization. Many thanks for letting us know about this work, we will properly mention this work in the next version of our paper.

Contributing

We use GitHub issues for tracking requests and bugs.

Discord Server

Please visit Unitary Fund's Discord server, where you can find a channel for graphix to ask questions.

Core Contributors

Dr. Shinichi Sunami (University of Oxford)

Masato Fukushima (University of Tokyo, Fixstars Amplify)

Acknowledgements

We are proud to be supported by unitary fund microgrant program.

unitary-fund

Special thanks to Fixstars Amplify:

amplify

License

Apache License 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

graphix-0.2.3.tar.gz (52.7 kB view details)

Uploaded Source

Built Distribution

graphix-0.2.3-py3-none-any.whl (48.4 kB view details)

Uploaded Python 3

File details

Details for the file graphix-0.2.3.tar.gz.

File metadata

  • Download URL: graphix-0.2.3.tar.gz
  • Upload date:
  • Size: 52.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for graphix-0.2.3.tar.gz
Algorithm Hash digest
SHA256 8c770c745b62a7aa388c45cf6b2802f05742b84fdc205200ddc4dd7b5ebbc347
MD5 0e2840921811c1074ff3d74758b01ea4
BLAKE2b-256 942d1fc573e3130dbe8dc42f2d95c7b41a6638679539d8c989bef719b47d3736

See more details on using hashes here.

File details

Details for the file graphix-0.2.3-py3-none-any.whl.

File metadata

  • Download URL: graphix-0.2.3-py3-none-any.whl
  • Upload date:
  • Size: 48.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for graphix-0.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 9119509aad5a6eff5d064758820cf5a1c2b05f91ac9d4959455640a1845b6100
MD5 bd99551f5c1209c8e7f34e79ef32f1c6
BLAKE2b-256 02589924128abc199c67acfcfc98afed656c62909431973535f9aedac50fea34

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page