Decoding Quantum LDPC Codes Using Graph Neural Networks

Autor:	Ninkovic, Vukan, Kundacina, Ognjen, Vukobratovic, Dejan, Häger, Christian, Amat, Alexandre Graell i
Rok vydání:	2024
Předmět:	Quantum Physics Computer Science - Information Theory Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted for GLOBECOM 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.05170 Zobrazit plný text záznamu View this record from Arxiv