Machine Learning Approaches to Decoding Topological Quantum Codes

2026-08-16

AI Summary

This study addresses the accuracy and scalability challenges in real-time decoding of topological quantum codes by presenting a systematic review of machine learning-based decoding methods. By integrating discriminative, generative, and reinforcement learning paradigms, this work establishes architectural principles for neural decoders that balance expressive power with low-latency requirements. Furthermore, the paper surveys benchmarks from memory experiments and recent advances in real-time decoding, identifying critical pathways to reconcile performance with practical deployment constraints. Ultimately, this review delineates future research directions and provides essential theoretical foundations and technical guidance to advance the practical realization of fault-tolerant quantum computing.

Abstract

Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information. Building fault-tolerant architectures requires increasing the code distance, which in turn places growing demands on decoding accuracy, scalability, and practical deployability. While a wide range of decoding algorithms have been proposed and demonstrated, achieving reliable, scalable, and real-time decoding remains a significant challenge. Machine-learning (ML) approaches are particularly well suited to this setting, as quantum error decoding is fundamentally a problem of processing large volumes of classical data with complex spatiotemporal correlations. This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations. We first frame decoding as a learning problem and outline key paradigms, including discriminative, generative, and reinforcement-learning formulations. We then introduce the neural network building blocks that underpin most contemporary neural decoders and discuss how these components can be integrated to balance expressivity, scalability, and latency. Building on this architectural perspective, we review recent progress and benchmarks in neural decoding for memory experiments, and discuss real-time decoding, open challenges, and future directions toward scalable fault-tolerant quantum computing.

Problem

• Topological Quantum Codes
• Quantum Error Correction
• Real-time Decoding
• Scalability
• Machine Learning

Innovation

• Topological Quantum Codes
• Neural Decoders
• Real-time Decoding
• Machine Learning
• Quantum Error Correction