Decoding Quantum LDPC Codes Using Graph Neural Networks
This addresses decoding challenges in quantum error correction, offering a novel approach with potential improvements in efficiency for quantum computing applications.
The paper tackles decoding Quantum LDPC codes by proposing a Graph Neural Network-based method, achieving excellent performance and low complexity compared to conventional and neural-enhanced decoders.
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.