Decoding Quantum LDPC Codes Using Graph Neural Networks

• URL: http://arxiv.org/abs/2408.05170v1
• Date: Fri, 9 Aug 2024 16:47:49 GMT
• Title: Decoding Quantum LDPC Codes Using Graph Neural Networks
• Authors: Vukan Ninkovic, Ognjen Kundacina, Dejan Vukobratovic, Christian Häger, Alexandre Graell i Amat,
• Abstract summary: We propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs) The proposed GNN-based QLDPC decoder exploits the sparse graph structure of QLDPC codes and can be implemented as a message-passing decoding algorithm.
• Score: 52.19575718707659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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