发表机构
School of Electrical and Electronic Engineering, University College Dublin; Department of Computer Architecture and Automatics, Complutense University of Madrid（都柏林大学学院电气与电子工程学院; 马德里康普顿斯大学计算机体系结构与自动系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出两阶段解码框架，先用BP快速处理，不收敛时过滤振荡比特并交由Tesseract纠错，在保持逻辑错误率下大幅降低复杂度，扩展节点减少至少5倍。
AI 中文摘要
量子低密度奇偶校验（QLDPC）码代表了量子计算中纠错的一种有前景的方法。最近提出的Tesseract解码器使用A*搜索算法，保证找到最可能的错误模式。然而，Tesseract的实际实现通常涉及一个极大的图，且搜索固有的顺序性导致高计算开销和长运行时间。为了提高解码效率，我们提出了一种两阶段解码框架。首先，置信传播（BP）解码器高效地处理综合征。该步骤生成硬判决（二进制错误向量）和软信息（每个量子比特的置信度水平）。在BP不收敛的情况下，门控机制检查BP解码器的输出，以识别并过滤掉置信度值振荡的量子比特。在这些情况下，精炼后的输出作为Tesseract的输入，然后Tesseract尝试识别并纠正任何残余错误。这种混合方法利用了BP解码的高速性，并将更具挑战性的解码实例委托给Tesseract。数值结果表明，在保持独立Tesseract的逻辑错误率（LER）的同时，整体解码复杂度显著降低。在所有测试的物理错误率下，所提出的方法在Tesseract中扩展节点数量至少减少5倍，在物理错误率p=0.05时，对于[[126, 12, d < 11]] T1码，峰值减少接近15倍，对于[[72, 12, 6]]双周期双变量（BB）码，在p=0.05时减少约8.8倍。
英文摘要
Quantum low-density parity-check (QLDPC) codes represent a promising approach for error correction in quantum computing. The recently proposed Tesseract decoder uses the A* search algorithm that guarantees finding the most likely error pattern. However, practical implementations of Tesseract often involve an extremely large graph, and the inherently sequential nature of the search results in high computational overhead and long runtime. To improve decoding efficiency, we propose a two-stage decoding framework. First, a belief propagation (BP) decoder efficiently processes the syndrome. This step generates hard decisions (a binary error vector) and soft information (per-qubit confidence levels). In non-convergent BP cases, a gating mechanism examines the BP decoder's output to identify and filter out qubits with oscillating confidence values. In these cases, the refined output serves as input to Tesseract, which then attempts to identify and correct any residual errors. This hybrid approach leverages the high speed of BP decoding and delegates the more challenging decoding instances to Tesseract. Numerical results demonstrate a substantial reduction in overall decoding complexity while maintaining the logical error rate (LER) of the stand-alone Tesseract. Across all tested physical error rates, the proposed method achieves at least 5x reduction in the number of expanded nodes in the Tesseract, with a peak reduction of nearly 15x at a physical error rate of p = 0.05 for the [[126, 12, d < 11]] T1 code, and approximately 8.8x at p = 0.05 for the [[72, 12, 6]] bicycle bivariate (BB) code.
CommentsAccepted for publication at IEEE PIMRC 2026
