High-Performance Reinforcement-Learned BP Decoding of Quantum LDPC Codes

2026/07/27 by Mohsen Moradi, Vahid Nourozi, Taejoon Kim +2
#cs.IT #math.IT

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Abstract

Belief-propagation (BP) decoding is attractive for quantum low-density parity-check (QLDPC) codes because it uses local message passing on sparse Tanner graphs. However, conventional flooding BP often stalls due to stabilizer degeneracy and short cycles. Reinforcement-learning-based sequential variable-node scheduling (RL-S), which learns the update order offline, has shown that adaptive scheduling can improve BP convergence. In this paper, we extend this idea with a second-order local update decoder, RL-S2LU. The proposed decoder preserves BP locality and low complexity, while numerical results show significant error-correction gains over conventional BP and the considered BP-OSD-10 baseline.

Citations

• Learning to Decode Quantum LDPC Codes via Cluster-Based Sequential Belief Propagation
• Reinforcement Learning for Sequential Decoding of Generalized LDPC Codes
• Quantum Tanner codes
• RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes
• Asymptotically Good Quantum and Locally Testable Classical LDPC Codes
• Quantum LDPC Codes With Positive Rate and Minimum Distance Proportional to the Square Root of the Blocklength
• Stabilizer Codes and Quantum Error Correction

Related

• Learning to Decode Quantum LDPC Codes via Cluster-Based Sequential Belief Propagation
• Restart Belief: A General Quantum LDPC Decoder
• Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation
• Accelerating BP-based decoders for QLDPC Codes with Local Syndrome-Based Preprocessing
• Graph Neural Networks for Enhanced Decoding of Quantum LDPC Codes