Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding
This provides a scalable decoding solution for quantum Tanner codes, which are important for quantum error correction, though it appears incremental as it builds on existing decoding techniques.
The paper tackles the problem of decoding quantum Tanner codes by introducing a soft-output guessing random additive noise decoding (SOGRAND) framework combined with ordered statistic decoding (OSD) post-processing, resulting in up to three orders of magnitude improvement in logical error rate compared to standard methods.
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.