arXiv cs.AI· Hong-Ye Hu, Zhenlai Wang, Binzhao Luo, Tao Xiang, Chen Zhao·· 14 小时前AI 评分29
神经分诊解码实现超高速率量子 LDPC 码的快速准确解码
Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes
AI 导读
研究者提出神经分诊解码（Triage）框架，用综合征一致性自适应分配神经计算，可解码编码数百个逻辑比特的超高速率 qLDPC 码（含 lifted-product 与 Kasai 码）。其精度与接近最优的算法解码器相当，解码延迟降低约一到两个数量级。该框架源于对精确逐比特边缘解码器的分析，揭示出与码距无关的综合征不一致错误地板，并显示神经网络解码器对误差先验失配具有鲁棒性。
正文 · 原文
Abstract:Quantum error correction is essential for fault-tolerant quantum computing. High-rate quantum low-density parity-check (qLDPC) codes have emerged as a promising approach to reducing the substantial physical qubit overhead of quantum error correction, enabling encoding rates exceeding $1/2$ in blocks of approximately one thousand physical data qubits. However, their practical implementation requires decoders that combine high accuracy with low latency to keep pace with quantum hardware. Existing decoders of high-rate qLDPC codes can achieve high accuracy but often incur substantial computational latency, whereas neural network decoders have yet to be demonstrated for high-rate qLDPC codes. Here, we introduce \emph{neural triage decoding}, a framework that uses syndrome consistency to adaptively allocate neural computation. A local neural message-passing network rapidly predicts error corrections, additional iterations reduce residual syndrome violations, and a rarely invoked diversification stage searches for consistent corrections to the remaining shots. We demonstrate that the \texttt{Triage} decoder can decode ultra-high-rate qLDPC codes encoding a few hundred logical qubits, including lifted-product and Kasai codes, achieving state-of-the-art accuracy comparable to the near optimal algorithmic decoders while reducing decoding latency by approximately one to two orders of magnitude. Our framework is inspired by a rigorous analysis of the exact bitwise marginal decoder, revealing a distance-independent syndrome-inconsistent error floor. In addition, we show the robustness of neural network decoders to mismatched error priors. Our results establish adaptive neural decoding as a promising route towards fast, accurate, and scalable quantum error correction in the near term.
| Comments: | 5 pages, 4 figures with Methods. We will release the fully supplementary information in the second update soon |
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.40490 [quant-ph] |
| (or arXiv:2610.40490v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40490 arXiv-issued DOI via DataCite |
Submission history
From: Hong-Ye Hu [view email]
[v1]
Thu, 29 Oct 2026 17:16:38 UTC (3,447 KB)
来源：arXiv cs.AI · arxiv.org