This work presents a hardware-efficient and fully parallelizable decoder for quantum LDPC codes that leverages belief propagation (BP) with a speculative post-processing strategy inspired by classical Chase decoding algorithm. By monitoring bit-level oscillation patterns during BP, our method identifies unreliable bits and generates multiple candidate vectors to selectively flip syndromes. Each modified syndrome is then decoded independently using short-depth BP, a process we refer to as BP-SF (syndrome flip). This design eliminates the need for costly Gaussian elimination used in the current BP-OSD approaches. Our implementation achieves logical error rates comparable to or better than BP-OSD while offering significantly lower latency due to its high degree of parallelism for a variety of bivariate bicycle codes. Evaluation on the [[144,12,12]] bivariate bicycle code shows that the proposed decoder reduces average latency to approximately $70\%$ of BP-OSD. When post-processing is parallelized the average latency is reduced by $55\%$ compared to the single process implementation, with the maximum latency reaching as low as $18\%$. These advantages make it particularly well-suited for real-time and resource-constrained quantum error correction systems.

翻译：本文提出一种硬件高效且完全可并行化的量子LDPC码解码器，该解码器采用置信传播算法，并结合受经典Chase解码算法启发的推测性后处理策略。通过监测BP过程中的比特级振荡模式，本方法能识别不可靠比特并生成多个候选向量以选择性翻转校验子。每个修正后的校验子随后通过短深度BP独立解码，该过程我们称为BP-SF。此设计消除了当前BP-OSD方法中所需的高斯消元计算。对于多种双变量自行车码，我们的实现方案在达到与BP-OSD相当或更优的逻辑错误率的同时，凭借高度并行化特性显著降低了延迟。在[[144,12,12]]双变量自行车码上的评估表明，所提解码器将平均延迟降低至BP-OSD的约$70\%$。当后处理过程并行化时，相比单进程实现方案平均延迟降低$55\%$，最大延迟可低至$18\%$。这些优势使其特别适用于实时且资源受限的量子纠错系统。