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This research introduces novel Belief Propagation decoders for surface codes that successfully achieve error-correction thresholds, matching the performance of Minimum Weight Perfect Matching under depolarizing noise. By shifting message exchange to the decoding graph rather than the Tanner graph, the study enables scalable, hardware-accelerated implementations critical for fault-tolerant quantum computing.
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Achieving Thresholds via Standalone Belief Propagation on Surface Codes

arXiv2 min read

arXiv:2603.05381v2 Announce Type: replace Abstract: The standard approach to Belief Propagation (BP) decoding passes messages on the Tanner graph of the quantum error-correcting (QEC) code, and is known to lack a threshold for the surface code. We propose novel BP decoders that exchange messages on the decoding graph and obtain thresholds (both for code capacity and circuit-level noise) via standalone BP for the surface code under depolarizing noise. Our approach, similarly to the minimum weight perfect matching (MWPM) decoder, is applicable to any graphlike QEC code. The thresholds observed with our decoders are close to those obtained by MWPM. This result opens the path towards scalable hardware-accelerated implementations of MWPM-compatible decoders.

Abstract:The standard approach to Belief Propagation (BP) decoding passes messages on the Tanner graph of the quantum error-correcting (QEC) code, and is known to lack a threshold for the surface code. We propose novel BP decoders that exchange messages on the decoding graph and obtain thresholds (both for code capacity and circuit-level noise) via standalone BP for the surface code under depolarizing noise. Our approach, similarly to the minimum weight perfect matching (MWPM) decoder, is applicable to any graphlike QEC code. The thresholds observed with our decoders are close to those obtained by MWPM. This result opens the path towards scalable hardware-accelerated implementations of MWPM-compatible decoders.

arXiv:2603.05381 [quant-ph]

(or arXiv:2603.05381v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2603.05381 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 195 words

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