AI 中文总结
研究针对量子低密度奇偶校验码BP解码性能受限问题，提出基于聚类的RL-S扩展方法，将VN划分为聚类并行更新，引入特定聚类状态表示并开发相关过程及更新，在减少调度决策数的同时保留误码率优势，实现延迟与并行性的良好权衡。
AI 中文摘要
量子低密度奇偶校验（QLDPC）码的置信传播（BP）解码因其低复杂度而具有吸引力，但其性能常受短循环、简并性和收敛失败的限制。基于强化学习的顺序变量节点（VN）调度（RL-S）可通过学习状态依赖的更新顺序改进BP解码，但该方法每次仅更新一个VN，迭代内并行性有限。本文提出基于聚类的RL-S扩展方法，将VN划分为固定聚类，RL智能体每次选择一个聚类并行更新其中所有VN。为使表格状态空间适用于大聚类规模，引入基于局部失配权重归一化直方图的置换不变聚类状态并量化，使聚类状态数取决于量化分辨率而非聚类大小。还开发了相应的聚类级马尔可夫决策过程、奖励函数和Q学习更新。对代表性QLDPC码的数值结果表明，该方法在保留VN级学习顺序调度大部分误码率优势的同时，大幅减少每次BP迭代的调度决策数，实现了延迟与并行性的良好权衡。
英文摘要
Belief-propagation (BP) decoding for quantum low-density parity-check (QLDPC) codes is attractive due to its low complexity, but its performance is often limited by short cycles, degeneracy, and convergence failures. Recently, reinforcement-learning-based sequential variable-node (VN) scheduling (RL-S) was shown to improve BP decoding by learning state-dependent update orders. However, the VN-by-VN nature of that approach offers limited within-iteration parallelism, since only one VN is updated at a time. In this paper, we propose a cluster-based extension of RL-S for QLDPC codes. The VNs are partitioned into fixed clusters, and at each scheduling step the RL agent selects one cluster to update, after which all VNs in that cluster are updated in parallel using the same pre-update incoming messages. To keep the tabular state space practical for large cluster sizes, we introduce a permutation-invariant cluster state based on a normalized histogram of local mismatch weights, followed by quantization. This representation makes the number of cluster states depend on the quantization resolution rather than the cluster size. We also develop the corresponding cluster-level Markov decision process, reward function, and Q-learning update. Numerical results on representative QLDPC codes show that the proposed clustered learned scheduling preserves most of the error-rate benefit of VN-level learned sequential scheduling while substantially reducing the number of scheduling decisions per BP iteration, thereby providing an attractive latency-parallelism tradeoff.
