arXiv cs.AI· Anil Bilgin, Wanda Hou, Pranav Srinivas, Yi-Zhuang You·· 11 小时前AI 评分31
GraphMP：面向量子 LDPC 码的搜索知识蒸馏解码器模型工厂
Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
AI 导读
GraphMP 是一种基于学习的消息传递解码器，面向量子 LDPC 码，无需微调即可跨码族、码长、物理错误率和噪声模型迁移。它用局部消息传递保持计算可扩展，并由预测引导 beam search 逐步修正候选错误模式，以更多解码时间换取更低逻辑错误率。研究提出“解码器模型工厂”范式，用模型引导搜索产生的已验证轨迹做知识蒸馏，将解码计算转化为可复用训练数据，并随噪声变化持续更新模型。
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