{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"PyMatching is a fast Python/C++ library for decoding quantum error correcting codes using the Minimum Weight Perfect Matching (MWPM) decoder, designed to find the most probable set of errors from syndrome measurements.","skillfed_tags":["quantum-computing","error-correction","graph-algorithms"],"use_cases":["Decode surface code circuits in quantum error correction simulations by configuring matching graphs from syndrome data.","Estimate the threshold of an error correcting code by running multiple decoding trials with varying noise levels.","Decode subsystem codes, honeycomb codes, or 2D hyperbolic codes by configuring custom weighted graphs.","Perform correlated matching on circuits where error correlations are present between graphlike errors.","Build custom quantum error correction workflows by adding edges individually to a matching graph."],"what_it_does":"PyMatching decodes quantum error correcting codes by solving the minimum-weight perfect matching problem on detector graphs derived from syndrome measurements. It is built on a sparse blossom algorithm that directly finds minimum-weight paths between detection events, avoiding the computational cost of all-to-all shortest-path searches. The library works with arbitrary weighted graphs (with or without boundaries) and can load matching graphs from check matrices, networkx graphs, or by adding edges individually using scipy, numpy, networkx, and matplotlib for numerical and graph operations.\n\nThe package is designed to decode surface codes and other code families (subsystem codes, honeycomb codes, 2D hyperbolic codes) in quantum error correction simulations. It can be configured with custom weighted graphs and integrates with external libraries for circuit-level noise simulation. As of v2.3, it also supports correlated matching to handle errors more accurately. Version 2 includes a substantially faster blossom implementation than earlier releases.","worth_installing":"Yes. PyMatching is actively maintained, has no known vulnerabilities, uses a permissive Apache 2 license, and is the standard decoder for surface codes in quantum error correction research. Install friction is moderate but manageable with prebuilt wheels. Choose it if you are working with quantum error correction; skip it if you have no quantum computing workflow."},"id":"pymatching","links":{"html":"https://skillfed.io/packages/pymatching","md":"https://skillfed.io/packages/pymatching.md","pypi":"https://pypi.org/project/pymatching/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-22","license_spdx":null,"license_treatment":"permissive","name":"PyMatching","python_support":"supports_current","summary":"A package for decoding quantum error correcting codes using minimum-weight perfect matching."},"popularity":{"monthly_downloads":184387,"position":10039,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.4.0"}