Degenerate Viterbi decoding

TL;DR

This paper introduces a linear-time decoding algorithm for quantum convolutional codes that efficiently identifies the most probable degenerate error classes, improving error correction performance.

Contribution

The paper presents a novel decoding algorithm that optimizes over classes of degenerate errors, unlike previous methods that focused on individual errors.

Findings

Significantly lower block error rate with the new decoding algorithm

Algorithm runs in linear time relative to the number of qubits

Monte Carlo simulations validate improved performance

Abstract

We present a decoding algorithm for quantum convolutional codes that finds the class of degenerate errors with the largest probability conditioned on a given error syndrome. The algorithm runs in time linear with the number of qubits. Previous decoding algorithms for quantum convolutional codes optimized the probability over individual errors instead of classes of degenerate errors. Using Monte Carlo simulations, we show that this modification to the decoding algorithm results in a significantly lower block error rate.