Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding

2026-03-18

AI Summary

This work addresses the challenges posed by trapping sets and short cycles in standard belief propagation (BP) decoding of quantum Tanner codes, which often lead to poor convergence and high logical error rates. To overcome these limitations, the authors propose a generalized low-density parity-check (LDPC) decoding framework that integrates soft-output guessing random additive noise decoding (SOGRAND) with ordered statistics decoding (OSD). This approach introduces, for the first time, a soft-output guessing mechanism into quantum Tanner code decoding, leveraging iterative soft information to enhance convergence and employing OSD as a post-processing step to substantially improve error correction capability. Experimental results demonstrate that the proposed method reduces logical error rates by up to three orders of magnitude compared to conventional BP+OSD decoding, offering a promising and efficient decoding pathway toward scalable fault-tolerant quantum computation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Large-scale security measurements

Abstract

We introduce a generalized low-density parity-check decoding framework for quantum Tanner codes utilizing soft-output guessing random additive noise decoding (SOGRAND). By soft-output decoding entire component codes, we mitigate trapping sets and cycles, resulting in improved convergence. SOGRAND, combined with ordered statistic decoding (OSD) post-processing, outperforms the standard belief propagation plus OSD baseline by up to three orders of magnitude in logical error rate, providing a way forward for scalable decoding of the emerging class of Tanner-code-based quantum codes.

Problem

• quantum Tanner codes
• trapping sets
• decoding
• belief propagation
• logical error rate

Innovation

• SOGRAND
• quantum Tanner codes
• soft-output decoding
• trapping set mitigation
• ordered statistic decoding