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

TL;DR

Sparse Blossom is a fast, efficient implementation of the minimum-weight perfect matching decoder for quantum error correction, significantly reducing decoding time to match syndrome data generation rates in superconducting quantum computers.

Contribution

We introduce Sparse Blossom, a novel variant of the blossom algorithm that directly addresses quantum decoding, avoiding all-to-all searches and enabling microsecond-scale decoding.

Findings

Processes syndrome data in less than one microsecond for surface codes

Handles 0.1% circuit-level depolarising noise effectively

Open-source implementation in PyMatching v2

Abstract

In this work, we introduce a fast implementation of the minimum-weight perfect matching (MWPM) decoder, the most widely used decoder for several important families of quantum error correcting codes, including surface codes. Our algorithm, which we call sparse blossom, is a variant of the blossom algorithm which directly solves the decoding problem relevant to quantum error correction. Sparse blossom avoids the need for all-to-all Dijkstra searches, common amongst MWPM decoder implementations. For 0.1% circuit-level depolarising noise, sparse blossom processes syndrome data in both $X$ and $Z$ bases of distance-17 surface code circuits in less than one microsecond per round of syndrome extraction on a single core, which matches the rate at which syndrome data is generated by superconducting quantum computers. Our implementation is open-source, and has been released in version 2 of the PyMatching library.