从搜索中蒸馏知识：面向量子 LDPC 码的解码器模型工厂
Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
Anil Bilgin · Wanda Hou · Pranav Srinivas · Yi-Zhuang You
中文摘要
GraphMP 是一种基于学习的消息传递（message passing）解码器，用于量子低密度奇偶校验（LDPC）码，无需微调即可跨码族、码尺寸、物理错误率和噪声模型迁移。它采用局部消息传递，使计算随解码图增长仍可扩展；在其预测引导下，beam search 逐步精化候选错误模式，以额外解码时间换取更低逻辑错误率。解码器模型工厂（decoder model factory）这一范式，用早期模型在解码中产生的经验训练后续模型：模型引导的搜索提供经校验的轨迹用于知识蒸馏（knowledge distillation），把解码计算转化为可复用训练数据。反复收集与蒸馏使解码器逐代提升，并随噪声变化调整策略。这些结果支持一种量子—经典耦合架构，包含两个交互循环：快速解码保护量子信息，较慢的学习从运行经验更新模型。长期目标是可扩展的解码器模型工厂，产出高性能、低延迟解码器并持续适应演进的量子硬件。
关键要点
- 01GraphMP 无需微调即可跨码族、码尺寸、错误率和噪声模型迁移
- 02局部消息传递保证解码图增大时计算可扩展
- 03beam search 以更多解码时间换取更低逻辑错误率
- 04模型工厂用解码经验蒸馏训练后续模型，逐代提升性能
- 05提出量子—经典双循环架构：快速解码与慢速学习协同
解读
尚无解读。
原始英文摘要
arXiv:2610.41398v1 Announce Type: cross 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.