Awesome
<img src="https://github.com/TeamGraphix/graphix/raw/master/docs/logo/black_with_name.png" alt="logo" width="550">
Graphix is a measurement-based quantum computing (MBQC) software package, featuring
- the measurement calculus framework with integrated graphical rewrite rules for Pauli measurement preprocessing
- circuit-to-pattern transpiler, graph-based deterministic pattern generator and manual pattern generation
- flow, gflow and pauliflow finding tools and graph visualization based on flows (see below)
- statevector, density matrix and tensornetwork pattern simulation backends
- QPU interface and fusion network extraction tool
Installation
Install graphix with pip:
pip install graphix
Install together with device interface:
pip install graphix[extra]
this will install graphix and inteface for IBMQ and Perceval to run MBQC patterns on superconducting and optical QPUs and their simulators.
Using graphix
generating pattern from a circuit
from graphix import Circuit
circuit = Circuit(4)
circuit.h(0)
...
pattern = circuit.transpile().pattern
pattern.draw_graph()
<small>note: this graph is generated from QAOA circuit, see our example code. Arrows indicate the causal flow of MBQC and dashed lines are the other edges of the graph. the vertical dashed partitions and the labels 'l:n' below indicate the execution layers or the order in the graph (measurements should happen from left to right, and nodes in the same layer can be measured simultaneously), based on the partial order associated with the (maximally-delayed) flow. </small>
preprocessing Pauli measurements
pattern.perform_pauli_measurements()
pattern.draw_graph()
<small>(here, the graph is visualized based on generalized flow.)</small>
simulating the pattern
state_out = pattern.simulate_pattern(backend="statevector")
and more..
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See demos showing other features of
graphix. -
You can try demos on browser with mybinder.org:
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Read the tutorial for more usage guides.
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For theoretical background, read our quick introduction into MBQC and LC-MBQC.
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Full API docs is here.
Citing
<!-- Update on the [arXiv paper](https://arxiv.org/pdf/2212.11975.pdf): [^1] [^1]: Following the release of this arXiv preprint, we were made aware of [Backens et al.](https://quantum-journal.org/papers/q-2021-03-25-421/) and related work, where graph-theoretic simplification (Pauli measurement elimination) of patterns were shown. Many thanks for letting us know about this work - at the time of the writing we were not aware of these important relevant works but will certainly properly mention in the new version; we are working on significant restructuring and rewriting of the paper and hope to update the paper soon. -->Shinichi Sunami and Masato Fukushima, Graphix. (2023) https://doi.org/10.5281/zenodo.7861382
Contributing
We use GitHub issues for tracking feature requests and bug reports.
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.
<p><a href="https://unitary.fund/grants.html"> <img src="https://user-images.githubusercontent.com/33350509/233384863-654485cf-b7d0-449e-8868-265c6fea2ced.png" alt="unitary-fund" width="150"/> </a></p>Special thanks to Fixstars Amplify:
<p><a href="https://amplify.fixstars.com/en/"> <img src="https://github.com/TeamGraphix/graphix/raw/master/docs/imgs/fam_logo.png" alt="amplify" width="200"/> </a></p>