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.

本研究解决了在量子学习模型中保护客户端私密数据的关键问题。通过引入量子同态加密方案，提出了一种可保证计算理论数据隐私的量子委托和联合学习框架。研究表明，该框架在学习和推理过程中具有明显较低的通信复杂性，对未来安全相关应用具有重要影响。

量子委托和联合学习通过量子同态加密