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PyMatching: A Python package for decoding quantum codes with minimum-weight perfect matching

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arxiv 2105.13082 v2 pith:UNUJOE5J submitted 2021-05-27 quant-ph

classification quant-ph
keywords pymatchingmatchingdecodinglocalmwpmquantumpythonalgorithm
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This paper introduces PyMatching, a fast open-source Python package for decoding quantum error-correcting codes with the minimum-weight perfect matching (MWPM) algorithm. PyMatching includes the standard MWPM decoder as well as a variant, which we call local matching, that restricts each syndrome defect to be matched to another defect within a local neighbourhood. The decoding performance of local matching is almost identical to that of the standard MWPM decoder in practice, while reducing the computational complexity approximately quadratically. We benchmark the performance of PyMatching, showing that local matching is several orders of magnitude faster than implementations of the full MWPM algorithm using NetworkX or Blossom V for problem sizes typically considered in error correction simulations. PyMatching and its dependencies are open-source, and it can be used to decode any quantum code for which syndrome defects come in pairs using a simple Python interface. PyMatching supports the use of weighted edges, hook errors, boundaries and measurement errors, enabling fast decoding and simulation of fault-tolerant quantum computing.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Syndrome aware mitigation of logical errors

    quant-ph 2025-12 conditional novelty 6.0 of 10

    Conditioning logical error mitigation on the measured error-correcting syndromes cuts sampling overhead exponentially and can make error correction useful above its standard pseudo-threshold.

  2. Nonlinear photonic architecture for fault-tolerant quantum computing

    quant-ph 2025-10 conditional novelty 6.0 of 10

    A photonic fault-tolerance architecture using deterministic single-photon nonlinearities maintains surface-code loss thresholds of up to 15.1% with QPC-encoded 2-chain resource states.

  3. QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A continually adapted neural pre-decoder reduces logical error rate and residual matching latency versus a fixed neural baseline across 110 OOD noise settings and zero-shot on Willow.

  4. Towards logical entanglement creation in trivalent planar architectures

    quant-ph 2026-07 conditional novelty 4.0 of 10

    Trivalent surface-code layouts can run lattice surgery without the extra data-qubit stripe used in four-valent layouts, but the simulated fidelity gain holds mainly at small distance or under assumed faster gates.

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