Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding

TL;DR

This paper presents a novel soft-output decoding framework for quantum Tanner codes using SOGRAND, significantly improving decoding performance and scalability by mitigating trapping sets and cycles.

Contribution

It introduces a generalized low-density parity-check decoding method with soft-output guessing, enhancing quantum Tanner code decoding beyond existing belief propagation techniques.

Findings

Outperforms belief propagation plus OSD by up to three orders of magnitude in logical error rate.

Mitigates trapping sets and cycles, leading to better convergence.

Enables scalable decoding for quantum Tanner codes.

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.