AI 中文总结
研究量子低密度奇偶校验码的BP解码问题，提出基于强化学习的二阶局部更新解码器RL-S2LU，保留BP局部性与低复杂度，相比传统BP及BP-OSD-10基线，在纠错性能上有显著提升。
AI 中文摘要
置信传播（BP）解码因其在稀疏 Tanner 图上使用局部消息传递而对量子低密度奇偶校验（QLDPC）码具有吸引力。然而，传统的泛洪BP常因稳定器简并和短循环而停滞。基于强化学习的顺序变量节点调度（RL-S）离线学习更新顺序，已表明自适应调度可改善BP收敛。本文用二阶局部更新解码器RL-S2LU扩展此想法。该解码器保留BP局部性和低复杂度，数值结果显示其纠错性能优于传统BP和BP-OSD-10基线。
英文摘要
Belief-propagation (BP) decoding is attractive for quantum low-density parity-check (QLDPC) codes because it uses local message passing on sparse Tanner graphs. However, conventional flooding BP often stalls due to stabilizer degeneracy and short cycles. Reinforcement-learning-based sequential variable-node scheduling (RL-S), which learns the update order offline, has shown that adaptive scheduling can improve BP convergence. In this paper, we extend this idea with a second-order local update decoder, RL-S2LU. The proposed decoder preserves BP locality and low complexity, while numerical results show significant error-correction gains over conventional BP and the considered BP-OSD-10 baseline.
