用于LDPC译码的量子增强置信传播
Quantum-enhanced belief propagation for LDPC decoding
AI总结：
针对低密度奇偶校验码置信传播译码的计算成本与纠错能力提升问题，提出以量子近似优化算法为预处理的量子增强置信传播算法，可加快收敛、降低块错误率，并研究了变分参数共享及后处理技术的码长缩放特性。
摘要：
低密度奇偶校验码的译码在第五代（5G）无线网络、卫星通信等众多现有技术中至关重要。得益于这类码的低密度特性，置信传播算法可实现快速译码。然而，该算法在大码长译码时的计算成本以及纠错能力方面仍有提升空间。本文提出量子增强置信传播（QEBP）算法，将量子近似优化算法（QAOA，Quantum Approximate Optimization Algorithm）作为置信传播的预处理步骤。我们对采用QAOA的伴随式译码进行精确仿真，其结果可引导置信传播算法，实现更快的收敛速度和更低的块错误率（BLER）。此外，我们通过重复码研究了不同伴随式之间共享变分参数的可能性，此场景下还可跨码长共享参数。我们通过转移矩阵方法优化译码成功概率，得到了1层QAOA的唯一一组变分参数对。随后，我们使用这些参数，比较了不同QAOA后处理技术的性能随码长的缩放规律。
英文摘要：
Decoding low-density parity-check codes is critical in many current technologies, such as fifth-generation (5G) wireless networks and satellite communications. The belief propagation algorithm allows for fast decoding due to the low density of these codes. However, there is scope for improvement to this algorithm both in terms of its computational cost when decoding large codes and its error-correcting abilities. Here, we introduce the quantum-enhanced belief propagation (QEBP) algorithm, in which the Quantum Approximate Optimization Algorithm (QAOA) acts as a pre-processing step to belief propagation. We perform exact simulations of syndrome decoding with QAOA, whose result guides the belief propagation algorithm, leading to faster convergence and a lower block error rate (BLER). In addition, through the repetition code, we study the possibility of having shared variational parameters between syndromes and, in this case, code lengths. We obtain a unique pair of variational parameters for level-1 QAOA by optimizing the probability of successful decoding through a transfer matrix method. Then, using these parameters, we compare the scaling of different QAOA post-processing techniques with code length.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
