拓扑量子码解码的机器学习方法
Machine Learning Approaches to Decoding Topological Quantum Codes
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中文总结 AI 辅助
本章综述拓扑量子码的机器学习解码方法，涵盖其范式、神经网络模块、实验进展，探讨实时解码挑战及容错量子计算未来方向。
中文摘要 AI 辅助
解码是量子纠错（QEC）的核心组成部分，它将稳定子测量结果转换为纠错操作，以抑制逻辑错误并保护逻辑量子信息。构建容错架构需要增大码距，这对解码的准确性、可扩展性和实际部署性提出了越来越高的要求。尽管已提出并验证了多种解码算法，但实现可靠、可扩展且实时的解码仍是一项重大挑战。机器学习（ML）方法特别适合该场景，因为量子错误解码本质上是处理具有复杂时空关联的大量经典数据的问题。本章综述了基于机器学习的量子错误解码方法，重点关注拓扑码，并强调架构原理、实际性能和实时性考量。我们首先将解码构建为一个学习问题，概述关键范式，包括判别式、生成式和强化学习范式。随后介绍支撑当前大多数神经解码器的神经网络构建模块，并讨论如何整合这些组件以平衡表达能力、可扩展性和延迟。基于该架构视角，我们综述了存储实验中神经解码的最新进展与基准，探讨了实时解码、开放挑战以及实现可扩展容错量子计算的未来方向。
英文摘要
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.
发表机构
- Yonsei University（延世大学）
- Samsung SDS（三星SDS）
机构由 AI 辅助整理，请以论文原文为准。
