In this paper, we propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs). Similar to the Belief Propagation (BP)-based QLDPC decoders, the proposed GNN-based QLDPC decoder exploits the sparse graph structure of QLDPC codes and can be implemented as a message-passing decoding algorithm. We compare the proposed GNN-based decoding algorithm against selected classes of both conventional and neural-enhanced QLDPC decoding algorithms across several QLDPC code designs. The simulation results demonstrate excellent performance of GNN-based decoders along with their low complexity compared to competing methods.

翻译：本文提出了一种基于图神经网络（GNN）的量子低密度奇偶校验（QLDPC）码新型解码方法。与基于置信传播（BP）的QLDPC解码器类似，所提出的基于GNN的QLDPC解码器利用了QLDPC码的稀疏图结构，并可实现为消息传递解码算法。我们在多种QLDPC码设计上，将所提出的基于GNN的解码算法与选定的传统及神经增强型QLDPC解码算法类别进行了比较。仿真结果表明，基于GNN的解码器在性能上表现优异，且与竞争方法相比具有较低的复杂度。