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Efficient soft-output decoders for the surface code

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arxiv 2405.07433 v2 pith:3BGWLAQT submitted 2024-05-13 quant-ph

Nadine Meister , Christopher A. Pattison , John Preskill This is my paper · ORCID
classification quant-ph
keywords codedecoderssoft-outputsurfacedecodingefficienterrorfault-tolerant
verification ladder T0 review T1 audit T2 compute T3 formal
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Decoders that provide an estimate of the probability of a logical failure conditioned on the error syndrome ("soft-output decoders") can reduce the overhead cost of fault-tolerant quantum memory and computation. In this work, we construct efficient soft-output decoders for the surface code derived from the Minimum-Weight Perfect Matching and Union-Find decoders. We show that soft-output decoding can improve the performance of a "hierarchical code," a concatenated scheme in which the inner code is the surface code, and the outer code is a high-rate quantum low-density parity-check code. Alternatively, the soft-output decoding can improve the reliability of fault-tolerant circuit sampling by flagging those runs that should be discarded because the probability of a logical error is intolerably large.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scalable decoding protocols for fast transversal logic in the surface code

    quant-ph 2025-05 conditional novelty 7.0 of 10

    The paper presents windowed decoding protocols that restore modularity and locality to decoding of fast transversal logic, enabling constant-time logical gates with scalable error correction.

  2. Syndrome aware mitigation of logical errors

    quant-ph 2025-12 conditional novelty 6.0 of 10

    Conditioning logical error mitigation on the measured error-correcting syndromes cuts sampling overhead exponentially and can make error correction useful above its standard pseudo-threshold.

  3. Efficient Post-Selection for General Quantum LDPC Codes

    quant-ph 2025-10 conditional novelty 6.0 of 10

    Cluster-size and cluster-LLR norm fractions from BP+LSD decoding suppress logical error rates by orders of magnitude at low abort rates on surface, bivariate bicycle, and hypergraph product codes.

  4. Machine-learned syndrome post-selection for reliable quantum error correction

    quant-ph 2026-07 conditional novelty 5.0 of 10

    Syndrome-only supervised learning can post-select quantum error correction runs, matching syndrome-weight filtering on simulations and outperforming it on experimental magic-state distillation data.

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