量子计算赋能联邦学习，提升隐私与效率。
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
- 融合量子计算与联邦学习，构建混合架构框架。
- 提出量子比特利用率等新指标，评估当前研究状态。
- 适合关注隐私安全与前沿交叉技术的研究者。
量子联邦学习（QFL）是新兴领域，利用量子计算（QC）提升去中心化联邦学习（FL）模型的可扩展性与效率。本文系统综述了FL与QC融合中的关键问题与解决方案，涵盖研究范式与新型分类体系，重点关注量子与联邦学习的局限性，如架构设计、噪声中等规模量子（NISQ）设备及隐私保护等。通过引入量子比特利用率效率与量子模型训练策略两项新指标，深入分析当前QFL研究现状。论文探讨关键技术进展与集成策略，聚焦混合量子-经典方法对联邦学习的影响。重点揭示量子计算优势在联邦学习中的应用：梯度隐藏、量子态纠缠、量子密钥分发、量子安全与量子增强差分隐私，构建更快速、更安全且隐私保障更强的框架。最后，提出未来研究方向，旨在填补现有空白，推动实用化、更高效安全的量子联邦学习模型发展。
原文摘要 · Abstract (English)
Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of the QFL research. This work explores key developments and integration strategies, along with the impact of QC on FL, keeping a sharp focus on hybrid quantum-classical approaches. The paper offers an in-depth understanding of how the strengths of QC, such as gradient hiding, state entanglement, quantum key distribution, quantum security, and quantum-enhanced differential privacy, have been integrated into FL to ensure the privacy of participants in an enhanced, fast, and secure framework. Finally, this study proposes potential future directions to address the identified research gaps and challenges, aiming to inspire faster and more secure QFL models for practical use.
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