Decoding Quantum LDPC Codes Using Graph Neural Networks
<|reference_start|>Decoding Quantum LDPC Codes Using Graph Neural Networks: 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.<|reference_end|>
@article{ninkovic2024decoding,
    title={Decoding Quantum LDPC Codes Using Graph Neural Networks},
    author={Vukan Ninkovic, Ognjen Kundacina, Dejan Vukobratovic, Christian
  H"ager, Alexandre Graell i Amat},
    journal={arXiv preprint arXiv:2408.05170},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05170},
    primaryClass={quant-ph cs.IT cs.LG math.IT}
}