Decoding Quantum LDPC Codes Using Graph Neural Networks
Paper in proceeding, 2024
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.
Quantum Low-Density Parity-Check Codes
Decoding Algorithms
Graph Neural Networks
Author
Vukan Ninkovic
University of Novi Sad
The Institute for Artificial Intelligence Research and Development of Serbia
Ognjen Kundacina
The Institute for Artificial Intelligence Research and Development of Serbia
Dejan Vukobratovic
University of Novi Sad
Christian Häger
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Alexandre Graell I Amat
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
23340983 (ISSN) 25766813 (eISSN)
3479-34849798350351255 (ISBN)
2024 IEEE Global Communications Conference, GLOBECOM 2024
Cape Town, South Africa,
Cape Town, South Africa,
INnovation and excellence in massive-scale COMmunications and information processING - INCOMING
European Commission (EC) (EC/H2020/856967), 2020-01-01 -- 2022-12-31.
Subject Categories (SSIF 2025)
Condensed Matter Physics
DOI
10.1109/GLOBECOM52923.2024.10901425