A graph-aware bounded distance decoder for all stabilizer codes

TL;DR

This paper introduces a graph-aware bounded distance decoding method applicable to all stabilizer codes, utilizing graphical representations and neural network pruning to improve decoding efficiency and performance.

Contribution

It presents a novel, general decoding framework for all stabilizer codes based on graph states and introduces an open-source library for practical implementation.

Findings

Successfully decoded non-CSS codes up to distance 11 under depolarizing errors.

Achieved near-optimal decoding for CSS codes like color and surface codes under bit-flip errors.

Reduced decoding runtime through strategic pruning and neural network techniques.

Abstract

We formulate a bounded distance decoding strategy applicable to all stabilizer codes including both CSS and non-CSS code-families. The framework emerges out of the local Clifford equivalence between arbitrary stabilizer states and graph states. Using the graphical representation of the stabilizers and the syndromes, we constitute the bounded distance decoding as an adaptable generalization of maximum likelihood decoding, ensuring correction of all errors with weights upper bounded by a target weight. We show that strategic pruning associated with a feed-forward network structure of the graph can reduce the search space and subsequently the runtime of the designed decoder. We demonstrate satisfactory performance of the bounded distance decoder in the case of the optimal non-CSS codes up to distance $d=11$ subjected to the depolarizing error on all qubits, and near-optimal decoding for the color and the surface codes, both belonging to the CSS family, under the bit-flip errors on the qubits. We also develop an open-source library, QGDecoder, enabling the graph-aware bounded distance decoding of arbitrary stabilizer codes.