AI 中文总结
针对量子纠错码拓扑限制解码算法性能的问题，提出噪声辅助集成解码框架，通过构建Tanner森林并结合综合软信息实现高精度解码，解码速度较现有方案提升数个数量级。
AI 中文摘要
均衡的解码器是现代容错量子计算发展的核心，但量子纠错码的固有拓扑结构会限制诸多已被深入研究的解码算法的性能。本研究提出噪声辅助集成解码（Noise Assisted Ensemble Decoding, NAED），这是一种高精度解码框架，在实时速度方面具有显著优势。NAED构建了一组Tanner森林，这些森林是从原始Tanner图中得到的无环子图，使用轻量级动态规划算法对每个Tanner森林执行精确推理。森林构建由综合软信息引导，该信息共同源自测量的校正子和信道统计，受控噪声扰动会生成多样化且具有信息性的解码矩阵列排序，用于整个集成的Tanner森林构建。基准测试结果表明，所提出的综合软信息驱动的Tanner森林构建与推理，能够取得优于或与BP+OSD0等最先进解码方案相当的解码性能，同时在电路级噪声下，每轮解码速度提升了数个数量级。
英文摘要
A well-balanced decoder has been central to the development of modern fault-tolerant quantum computing. However, the inherent topologies of quantum error correcting codes can limit the performance of many well-studied decoding algorithms. In this work, we introduce Noise Assisted Ensemble Decoding (NAED), a highly accurate decoding framework with a significant advantage in real-time speed. NAED constructs an ensemble of Tanner forests, obtained as acyclic subgraphs of the original Tanner graph, and performs exact inference on each Tanner forest using a lightweight dynamic programming algorithm. The forest construction is guided by synthetic soft information derived jointly from the measured syndrome and channel statistics, with controlled noise perturbations generating diverse yet informative decoding matrix column orderings for the Tanner forest construction across the ensemble. Our benchmark results show that the proposed synthetic soft information-driven construction and inference on the Tanner forests can achieve improved or comparable decoding performances to the state-of-the-art decoding solutions, such as BP+OSD$0$, while also providing orders-of-magnitude improvements in per-round decoding speed under circuit-level noise.
Comments8 figures, 5 tables, 22 pages
