General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes

TL;DR

This paper presents a general framework for designing fast, near-optimal machine learning decoders for topological stabilizer codes, emphasizing neural network structures and training criteria to enhance quantum error correction.

Contribution

It introduces conditions for near-optimal decoding, proposes a training criterion for limited data, and develops a convolutional neural network-based decoder leveraging topological code structures.

Findings

Improved decoding performance across various topological codes.

Effective neural network architectures for quantum error correction.

Enhanced robustness with limited training data.

Abstract

Quantum error correction is an essential technique for constructing a scalable quantum computer. In order to implement quantum error correction with near-term quantum devices, a fast and near-optimal decoding method is demanded. A decoder based on machine learning is considered as one of the most viable solutions for this purpose, since its prediction is fast once training has been done, and it is applicable to any quantum error correcting code and any noise model. So far, various formulations of the decoding problem as the task of machine learning have been proposed. Here, we discuss general constructions of machine-learning-based decoders. We found several conditions to achieve near-optimal performance, and proposed a criterion which should be optimized when a size of training data set is limited. We also discuss preferable constructions of neural networks, and proposed a decoder using spatial structures of topological codes using a convolutional neural network. We numerically show that our method can improve the performance of machine-learning-based decoders in various topological codes and noise models.