GraphMP：从搜索中蒸馏知识的量子 LDPC 码译码器模型工厂
Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
AI 导读
研究者提出 GraphMP，一种基于学习的量子低密度奇偶校验（LDPC）码消息传递译码器，可在不同码族、码规模、物理错误率和噪声模型间免微调迁移，并借助模型引导的 beam search 逐步细化候选错误图样以降低逻辑错误率。论文还提出译码器模型工厂范式，把搜索得到的已验证轨迹蒸馏为训练数据，逐代改进译码器，使其随噪声变化持续适应，目标是产出高性能、低延迟且可随量子硬件演进不断更新的译码器。
正文 · 原文
Abstract:We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code sizes, physical error rates, and noise models. GraphMP uses local message passing to keep computation scalable as the decoding graph grows. Guided by its predictions, beam search progressively refines candidate error patterns, trading additional decoding time for lower logical error rates. We develop the decoder model factory as a paradigm for training successive models on experience generated by earlier models during decoding. Model-guided search supplies verified trajectories for knowledge distillation, folding computation spent on decoding into reusable training data. Repeated collection and distillation improve the decoder across generations and adapt its strategy as the noise changes. These results motivate a coupled quantum--classical architecture with two interacting cycles: rapid decoding protects quantum information, while slower learning renews the models from operating experience. The long-term vision is a scalable decoder model factory that produces high-performance, low-latency decoders and continually adapts them to evolving quantum hardware.
| Subjects: | Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.41398 [quant-ph] |
| (or arXiv:2610.41398v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41398 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yi-Zhuang You [view email]
[v1]
Fri, 30 Oct 2026 08:09:24 UTC (1,356 KB)
来源：arXiv cs.AI · arxiv.org