Neural decoders for subsystem many-hypercube codes

Abstract:To maximize the potential of quantum error-correcting codes, it is essential to develop high-performance decoders. The subsystem many-hypercube (MHC) codes have been developed to achieve both high encoding rates and low-weight syndrome-measurements, but the introduction of gauge degrees of freedom makes decoding more challenging. In this work, we develop neural-network-based decoders for the subsystem MHC codes in a circuit-level noise model. We demonstrate that even the gauge-measurement information can be utilized for decoding by carefully arranging the syndrome-measurement sequence, improving the decoding performance. We further show that recurrent neural decoders outperform simple fully connected neural decoders, and can decode syndrome-measurement sequences longer than those used during training.

arXiv:2608.13975 [quant-ph]

(or arXiv:2608.13975v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2608.13975 Focus to learn more arXiv-issued DOI via DataCite

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