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.

翻译：量子学习模型有望在计算能力上超越经典领域。随着强大的量子服务器在云端可用，确保客户私有数据的保护变得至关重要。通过引入量子同态加密方案，我们提出了一个通用框架，该框架能够实现具有计算理论数据隐私保证的量子委托与联邦学习。我们证明，相较于基于盲量子计算的方案，该框架下的学习与推理过程具有显著降低的通信复杂度。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轻，因为服务器可以在不提取任何信息的情况下对加密的量子数据进行操作。我们进一步证明，监督学习中的某些量子加速优势在采用量子核方法的私有委托学习场景中得以保留。我们的研究结果为云端隐私保障的量子学习提供了有价值的指导，可能有益于未来的研究及安全相关应用。