用量子全同态加密实现安全的云端量子学习，保护数据隐私。
Quantum delegated and federated learning via quantum homomorphic encryption
- 基于量子全同态加密构建通用框架，支持私密量子委托与联邦学习。
- 通信开销显著低于盲量子计算方案，本地设备计算负担更轻。
- 保留量子加速优势，适合对数据安全要求高的量子机器学习应用。
量子学习模型有望在计算上超越经典方法。随着云上强大量子服务器的出现，保护客户端私有数据变得至关重要。通过引入量子全同态加密方案，我们提出一个通用框架，实现了具备计算理论保障的数据隐私的量子委托学习与联邦学习。该框架下，学习与推理的通信复杂度远低于基于盲量子计算的方案。此外，在所提出的量子联邦学习场景中，客户端本地量子设备的计算负担更小，因服务器可直接对加密量子数据进行操作而不泄露任何信息。我们进一步证明，某些监督学习中的量子加速可延续至采用量子核方法的私密委托学习场景。研究成果为云端隐私保障的量子学习提供了重要指引，可能推动未来研究与安全相关应用的发展。
原文摘要 · Abstract (English)
Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients' private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing. In addition, in the proposed quantum federated learning scenario, there is less computational burden on local quantum devices from the client side, since the server can operate on encrypted quantum data without extracting any information. We further prove that certain quantum speedups in supervised learning carry over to private delegated learning scenarios employing quantum kernel methods. Our results provide a valuable guide toward privacy-guaranteed quantum learning on the cloud, which may benefit future studies and security-related applications.
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