Localized statistics decoding for quantum low-density parity-check codes

Quantum low-density parity-check codes are a promising candidate for fault-tolerant quantum computing with considerably reduced overhead compared to the surface code. However, the lack of a practical decoding algorithm remains a barrier to their implementation. In this work, we introduce localized statistics decoding, a reliability-guided inversion decoder that is highly parallelizable and applicable to arbitrary quantum low-density parity-check codes. Our approach employs a parallel matrix factorization strategy, which we call on-the-fly elimination, to identify, validate, and solve local decoding regions on the decoding graph. Through numerical simulations, we show that localized statistics decoding matches the performance of state-of-the-art decoders while reducing the runtime complexity for operation in the sub-threshold regime. Importantly, our decoder is more amenable to implementation on specialized hardware, positioning it as a promising candidate for decoding real-time syndromes from experiments.

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Publication Details

Journal
Nature Communications
Published
2025-09-02
DOI
https://doi.org/10.1038/s41467-025-63214-7
Citations
19
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
47.14

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Localized statistics decoding for quantum low-density parity-check codes

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Nature Communications
Quantum Computing Algorithms and Architecture
47.14
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Localized statistics decoding for quantum low-density parity-check codes

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article en
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Abstract

Quantum low-density parity-check codes are a promising candidate for fault-tolerant quantum computing with considerably reduced overhead compared to the surface code. However, the lack of a practical decoding algorithm remains a barrier to their implementation. In this work, we introduce localized statistics decoding, a reliability-guided inversion decoder that is highly parallelizable and applicable to arbitrary quantum low-density parity-check codes. Our approach employs a parallel matrix factorization strategy, which we call on-the-fly elimination, to identify, validate, and solve local decoding regions on the decoding graph. Through numerical simulations, we show that localized statistics decoding matches the performance of state-of-the-art decoders while reducing the runtime complexity for operation in the sub-threshold regime. Importantly, our decoder is more amenable to implementation on specialized hardware, positioning it as a promising candidate for decoding real-time syndromes from experiments.

Nature CommunicationsVol. 16(1)
Helmholtz-Zentrum Berlin für Materialien und Energie (DE), Software Competence Center Hagenberg (Austria) (AT), Technical University of Munich (DE), Chalmers University of Technology (SE), Freie Universität Berlin (DE), University of Edinburgh (GB)
UK Research and Innovation, European Commission, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Knut och Alice Wallenbergs Stiftelse, Bundesministerium für Digitalisierung und Wirtschaftsstandort, Simons Institute for the Theory of Computing, University of California Berkeley, Engineering and Physical Sciences Research Council, Wallenberg Centre for Molecular and Translational Medicine, Wallenberg Center for Quantum Technology, Chalmers University of Technology
Openalex Percentile: Top 0%
Quantum Computing Algorithms and Architecture
47.14
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