当联邦学习遇见量子计算：调研与研究机会
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
AI总结：
本文调研了量子计算与联邦学习结合的新兴问题和解决方案，提出量子位利用效率和量子模型训练策略等新指标，探讨了量子计算在联邦学习中的应用及未来发展方向。
摘要：
量子联邦学习（QFL）是一个新兴领域，它利用量子计算（QC）的进步来提高去中心化联邦学习（FL）模型的可扩展性和效率。本文系统而全面地调研了当FL与QC相遇时出现的新兴问题和解决方案，从研究协议到一种新的分类法，特别关注量子和联邦学习的限制，如其架构、噪声中间尺度量子（NISQ）设备和隐私保护等。引入了两个新的指标，量子位利用效率和量子模型训练策略，我们对当前QFL研究的现状进行了深入分析。本文探讨了关键发展和整合策略，以及QC对FL的影响，始终关注混合量子-经典方法。本文深入理解了如何将QC的优势，如梯度隐藏、态纠缠、量子密钥分发、量子安全性和量子增强型差分隐私，整合到FL中，以确保在增强、快速和安全的框架中保护参与者的隐私。最后，本研究提出了潜在的未来方向，以解决识别出的研究空白和挑战，旨在激发更快和更安全的QFL模型在实际应用中的使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
