Sparse Blossom: correcting a million errors per core second with minimum-weight matching

Oscar Higgott, Craig Gidney · 2023 · Quantum 9, 1600 (2025)

| 编号 QE260 | 主题 译码算法 | 链接 arXiv:2303.15933 |

摘要

摘要保留原文（英文）

摘要取自 arXiv / 出版方元数据，未作翻译，以保证与原文一致。

We present sparse blossom, an algorithm for solving minimum-weight perfect matching with a worst-case cubic asymptotic runtime, but which in practice decodes surface code circuit-level noise so fast that decoding speed is no longer a bottleneck. Sparse blossom can process around a million errors per second on a single core. For a distance-17 surface code, it can decode both X and Z bases in under 1 microsecond per round of error correction, which matches the rate at which syndrome data is generated on superconducting quantum computers. Our implementation is available in version 2 of the open-source PyMatching Python package.

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