发表机构
Arizona State University; The University of Texas at Arlington（亚利桑那州立大学; 德克萨斯大学阿灵顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于强化学习的无冲突颜色聚类顺序BP解码方法，通过构建VN冲突图并着色，实现并行更新，在保持性能的同时大幅减少调度决策。
AI 中文摘要
量子低密度奇偶校验（QLDPC）码的置信传播（BP）解码因其低复杂度和低延迟而具有吸引力，但常常受到短环、退化性和收敛失败的限制。基于强化学习的顺序BP解码（RL-S）通过学习依赖于综合征的变量节点（VN）更新顺序来改进BP，但其逐VN的调度在迭代内并行性方面有限。在本文中，我们提出了RL-S的一种无冲突颜色聚类扩展。我们构建了一个VN冲突图，其中两个VN如果共享一个X型或Z型校验，则它们是相邻的，并对该图进行着色，使得相同颜色的VN具有不相交的校验邻域。这也防止了来自同一Tanner 4环或6环的VN被同时更新。在解码过程中，训练好的VN级Q表选择一个种子VN，所有具有相同颜色的其余VN使用相同的预批次消息并行更新。对于去极化信道上的[[288,12,18]]双变量自行车码，我们提出的解码器实现了接近VN级RL-S的块错误率性能，同时将每次BP迭代的调度决策从288个VN减少到11个颜色类别。
英文摘要
Belief-propagation (BP) decoding for quantum low-density parity-check (QLDPC) codes is attractive due to its low complexity and low latency, but it is often limited by short cycles, degeneracy, and convergence failures. Reinforcement-learning-based sequential BP decoding (RL-S) improves BP by learning a syndrome-dependent variable-node (VN) update order, but its VN-by-VN schedule has limited within-iteration parallelism. In this paper, we propose a conflict-free color-clustered extension of RL-S. We construct a VN conflict graph in which two VNs are adjacent if they share an X-type or Z-type check, and color this graph so that same-color VNs have disjoint check neighborhoods. This also prevents VNs from the same Tanner 4- or 6-cycle from being updated simultaneously. During decoding, the trained VN-level Q-table selects a seed VN, and all remaining VNs with the same color are updated in parallel using the same pre-batch messages. For the [[288,12,18]] bivariate-bicycle code over the depolarizing channel, our proposed decoder achieves block-error-rate performance close to VN-level RL-S while reducing the scheduling decisions from 288 VNs to 11 color classes per BP iteration.
