用进化算法优化量子纠错解码，提速降复杂度。
Evolutionary BP+OSD Decoding for Low-Latency Quantum Error Correction
- 用差分进化算法优化BP+OSD结构，端到端提升性能。
- 减少OSD频繁调用，复杂度显著降低，延迟更小。
- 适合对延迟敏感的量子计算系统，如表面码与QLDPC码。
容错量子计算中的量子误差纠正（QEC）需要在性能、复杂度和延迟之间取得平衡。然而，当前主流的置信传播（BP）结合有序统计解码（OSD）方法存在BP阶段迭代过多、OSD阶段复杂度高的问题。为此，我们提出一种通过差分进化（DE）算法优化的进化BP（EBP）解码器。利用DE无梯度特性，实现对EBP+OSD结构的端到端优化，以最大化整体性能。同时引入多目标选择规则，抑制OSD的频繁激活，显著降低复杂度开销。在表面码和量子低密度奇偶校验（QLDPC）码上的实验表明，相较于传统BP+OSD，EBP+OSD在保持优异解码性能的同时，显著降低了复杂度，尤其在严格低延迟场景下优势明显。
原文摘要 · Abstract (English)
Quantum error correction (QEC) for fault-tolerant quantum computing requires a balanced decoding solution that offers high performance, low complexity, and low latency. However, the de facto standard, belief propagation (BP) combined with ordered statistics decoding (OSD), suffers from excessive iterations in the BP stage and high complexity in the OSD stage. To address these challenges, we propose an evolutionary BP (EBP) decoder optimized via a differential evolution (DE) algorithm. By leveraging the gradient-free nature of DE, we enable end-to-end optimization of the EBP+OSD structure to maximize overall performance. In addition, a multi-objective selection rule is introduced to suppress frequent OSD activation, significantly reducing complexity overhead. Experimental results on surface codes and quantum low-density parity-check (QLDPC) codes demonstrate that EBP plus OSD simultaneously achieves superior decoding performance and substantially lower complexity compared to conventional BP plus OSD, particularly in stringent low-latency regimes.
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