Work

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

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2607.24891

Authors: Moradi, Mohsen, Nourozi, Vahid, Kim, Taejoon, Chou, Remi A., Mitchell, David G. M.

Institutions: Arizona State University, New Mexico State University, The University of Texas at Arlington

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Office of Naval Research

Awards: GOALI: CNS: Medium: Communication-Computation Co-Design for Rural Connectivtiy and Intelligence under Nonuniformity: Modeling, Analysis, and Implementation, Collaborative Research: NSF-AoF: CNS Core: Small: Towards Scalable and Al-based Solutions for Beyond-5G Radio Access Networks, G2318548222, CAREER: Sparse Graph-Based Codes for Network Data Compression, NSF Convergence Accelerator Track G: Combating Vulnerability and Unawareness in 5G Network Security

OpenAlex ID: W7171669918

Cites:

Cited by:

Related to: