PyMatching: A Python Package for Decoding Quantum Codes with Minimum-Weight Perfect Matching

Oscar Higgott
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引用次数: 74

Abstract

This article 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 neighborhood. The decoding performance of local matching is almost identical to that of the standard MWPM decoder in practice, while reducing the computational complexity. 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.
PyMatching:一个Python包,用于解码具有最小权重完美匹配的量子码
本文介绍PyMatching,这是一个快速的开源Python包,用于使用最小权重完美匹配(MWPM)算法解码量子纠错码。PyMatching包括标准的MWPM解码器以及一个变体,我们称之为局部匹配,它限制每个综合征缺陷与局部邻域内的另一个缺陷匹配。在实际应用中,局部匹配的译码性能与标准MWPM译码器几乎相同,同时降低了计算复杂度。我们对PyMatching的性能进行了基准测试,结果表明,对于错误校正模拟中通常考虑的问题大小,使用NetworkX或Blossom V的完整MWPM算法的实现比本地匹配快几个数量级。PyMatching及其依赖项是开源的,它可以使用简单的Python接口来解码任何量子代码,其中综合症缺陷成对出现。PyMatching支持使用加权边、钩子错误、边界和测量错误,实现快速解码,并模拟容错量子计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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