Neural decoders for subsystem many-hypercube codes

• URL: http://arxiv.org/abs/2608.13975v1
• Date: Fri, 14 Aug 2026 05:35:11 GMT
• Title: Neural decoders for subsystem many-hypercube codes
• Abstract summary: We develop neural-network-based decoders for subsystem many-hypercube codes in a circuit-level noise model.<n>We show that recurrent neural decoders outperform simple fully connected neural decoders, and can decode syndrome-measurement sequences longer than those used during training.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Decoding quantum low density parity check codes with diffusion [0.5855198111605814]
  We introduce a diffusion model framework to infer logical errors from syndrome measurements in quantum low-density parity-check codes.<n>We show that masked diffusion decoders are more accurate, often faster on average, and always faster in the worst case than other state-of-the-art decoders.
  arXiv  Detail & Related papers  (2025-09-26T13:46:52Z)
• Decoding for Punctured Convolutional and Turbo Codes: A Deep Learning Solution for Protocols Compliance [32.887114329215045]
  This paper proposes a unified long short-term memory (LSTM)-based neural decoder for punctured convolutional and Turbo codes.<n>The key component of the proposed LSTM-based neural decoder is puncturing-aware embedding, which integrates puncturing patterns directly into the neural network.
  arXiv  Detail & Related papers  (2025-02-21T14:00:14Z)
• Learning Linear Block Error Correction Codes [62.25533750469467]
  We propose for the first time a unified encoder-decoder training of binary linear block codes. We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient.
  arXiv  Detail & Related papers  (2024-05-07T06:47:12Z)
• A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction [0.0]
  We design and analyze a neural decoder based on an in-memory crossbar (IMC) architecture. We develop hardware-aware re-training methods to mitigate the fidelity loss. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated fault-tolerant QEC.
  arXiv  Detail & Related papers  (2023-07-18T17:46:33Z)
• The END: An Equivariant Neural Decoder for Quantum Error Correction [73.4384623973809]
  We introduce a data efficient neural decoder that exploits the symmetries of the problem. We propose a novel equivariant architecture that achieves state of the art accuracy compared to previous neural decoders.
  arXiv  Detail & Related papers  (2023-04-14T19:46:39Z)
• Optimizing Serially Concatenated Neural Codes with Classical Decoders [8.692972779213932]
  We show that a classical decoding algorithm is applied to a non-trivial, real-valued neural code. As the BCJR algorithm is fully differentiable, it is possible to train, or fine-tune, the neural encoder in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-12-20T15:40:08Z)
• Neural Belief Propagation Decoding of Quantum LDPC Codes Using Overcomplete Check Matrices [60.02503434201552]
  We propose to decode QLDPC codes based on a check matrix with redundant rows, generated from linear combinations of the rows in the original check matrix. This approach yields a significant improvement in decoding performance with the additional advantage of very low decoding latency.
  arXiv  Detail & Related papers  (2022-12-20T13:41:27Z)
• Denoising Diffusion Error Correction Codes [92.10654749898927]
  Recently, neural decoders have demonstrated their advantage over classical decoding techniques. Recent state-of-the-art neural decoders suffer from high complexity and lack the important iterative scheme characteristic of many legacy decoders. We propose to employ denoising diffusion models for the soft decoding of linear codes at arbitrary block lengths.
  arXiv  Detail & Related papers  (2022-09-16T11:00:50Z)
• Graph Neural Networks for Channel Decoding [71.15576353630667]
  We showcase competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes. The idea is to let a neural network (NN) learn a generalized message passing algorithm over a given graph. We benchmark our proposed decoder against state-of-the-art in conventional channel decoding as well as against recent deep learning-based results.
  arXiv  Detail & Related papers  (2022-07-29T15:29:18Z)
• Adversarial Neural Networks for Error Correcting Codes [76.70040964453638]
  We introduce a general framework to boost the performance and applicability of machine learning (ML) models. We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words. Our framework is game-theoretic, motivated by generative adversarial networks (GANs)
  arXiv  Detail & Related papers  (2021-12-21T19:14:44Z)
• Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding [19.48350605321212]
  Deep neural networks have been successfully applied in channel coding to improve the decoding performance. We propose doubly residual neural (DRN) decoder. DRN enables significant decoding performance improvement while maintaining low complexity.
  arXiv  Detail & Related papers  (2021-02-08T01:48:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.