graphix 0.3.2

Optimize and simulate measurement-based quantum computation

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 interface 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.standardize()
pattern.shift_signals()
pattern.draw_graph(flow_from_pattern=False)

See our example code to generate this pattern. 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.

preprocessing Pauli measurements (Clifford gates)

pattern.perform_pauli_measurements()
pattern.draw_graph()

(here, the visualization is based on generalized flow).

simulating the pattern

state_out = pattern.simulate_pattern(backend="statevector")

and more..

• See demos showing other features of graphix.

• Read the tutorial for more usage guides.

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

• Full API docs is here.

Projects using graphix

• Verphix: verified blind quantum computation and benchmarking.

Citing

Shinichi Sunami and Masato Fukushima, Graphix. (2023)

• Zenodo: https://doi.org/10.5281/zenodo.7861382
• arXiv: https://doi.org/10.48550/arXiv.2212.11975

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 (alphabetical order)

• Masato Fukushima (University of Tokyo, Fixstars Amplify)
• Maxime Garnier (Inria Paris)
• Thierry Martinez (Inria Paris)
• Sora Shiratani (University of Tokyo, Fixstars Amplify)
• Shinichi Sunami (University of Oxford)

Acknowledgements

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

Special thanks to Fixstars Amplify:

License

Apache License 2.0