Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding
Quantum Physics
2026-03-20 v1 Information Theory
math.IT
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.
Cite
@article{arxiv.2603.18318,
title = {Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding},
author = {Lukas Rapp and Muriel Médard and Eugene Tang and Ken R. Duffy},
journal= {arXiv preprint arXiv:2603.18318},
year = {2026}
}