LEAD: A Local Ensemble-Assisted Parallel Decoding Framework for Quantum Tanner Codes

TL;DR

LEAD is a novel parallel decoding framework for quantum Tanner codes that improves error correction performance and reduces decoding latency by leveraging local structures and topological symmetries.

Contribution

This work introduces LEAD, a structure-aware, highly parallelized decoding framework specifically designed for quantum Tanner codes, enhancing practical decoding efficiency.

Findings

LEAD achieves lower logical error rates than standard decoders.

LEAD significantly reduces decoding latency and iteration count.

Simulation results validate the effectiveness of LEAD in practical scenarios.

Abstract

Quantum Tanner codes are a recently developed family of quantum error-correcting codes characterized by favorable asymptotic performance characteristics. Despite their theoretical potential, practical decoding algorithms that effectively leverage their structural properties remain limited. This work introduces LEAD (Local Ensemble-Assisted Decoder), a structure-aware decoding framework tailored for quantum Tanner codes. The proposed scheme leverages the decomposable structure of Cayley complexes to project the global code onto overlapping local subcodes defined by vertex neighborhoods, where error probabilities are estimated in parallel. To ensure global consistency, LEAD utilizes the inherent topological symmetry of the complex and introduces a soft-information regularization mechanism to mitigate local overconfidence during information aggregation. This framework enables highly parallelized, low-complexity decoding that is intrinsically compatible with various local search heuristics. Simulation results demonstrate that LEAD achieves significantly lower logical error rates than standard decoding framework while substantially reducing the average decoding latency and iteration count.