量子联邦学习研究综述：融合、挑战与未来展望

A Comprehensive Survey on Integration, Challenges, and Future Prospects of Quantum Federated Learning

摘要: 量子联邦学习是量子计算与联邦学习交叉融合的前沿领域，旨在利用量子计算的并行性、纠缠等特性，解决分布式机器学习中的数据隐私、计算效率与模型性能等核心挑战。本文首先介绍了量子联邦学习产生的背景与核心定义，即多个量子客户端在保护数据隐私的前提下协作训练共享量子模型；然后从联邦架构、网络拓扑、通信方案、优化技术与安全机制五个维度系统梳理现有技术框架与分类体系；重点综述其在医疗健康、物联网与智能车联、卫星网络、元空间等关键领域的应用实践与性能优势；深入分析了当前面临的主要挑战，包括硬件限制与噪声、系统与数据的异质性、通信开销、安全与隐私威胁。最后，展望了量子错误缓解、个性化训练、鲁棒聚合算法、标准化评估基准等未来研究方向，为这一新兴领域的持续发展提供系统性参考。

Abstract: Quantum Federated Learning (QFL) is an emerging interdisciplinary field that integrates quantum computing with federated learning. It aims to leverage quantum properties such as superposition and entanglement to address core challenges in distributed machine learning including data privacy, computational efficiency, and model performance. This paper first outlines the background and core definition of QFL, which enables multiple quantum clients to collaboratively train a shared quantum model while preserving data privacy. Subsequently, we systematically review existing technical frameworks and taxonomies from five dimensions: federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms. Furthermore, we focus on surveying its application practices and performance advantages in critical domains such as healthcare, Internet of Things (IoT) & intelligent vehicular networks, satellite networks, and the metaverse. This paper provides an in-depth analysis of the current major challenges, including hardware limitations and noise, system and data heterogeneity, communication overhead, and security and privacy threats. Finally, we prospect future research directions such as quantum error mitigation, personalized training, robust aggregation algorithms, and standardized evaluation benchmarks, offering a systematic reference for the continued development of this burgeoning field.