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arXiv · 2608.13975

Neural decoders for subsystem many-hypercube codes

Ryota Nakai·Hayato Goto
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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.

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Ryota Nakai, Hayato Goto. 2026-08-14. Neural decoders for subsystem many-hypercube codes. https://arxiv.org/abs/2608.13975

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