Quantum machine learning in cloud environments requires protecting sensitive data while enabling remote computation. Here we demonstrate the first realistic implementations of a perfectly-secure quantum homomorphic encryption (QHE) scheme applied to quantum neural networks (QNN). Using efficient Clifford+$T$ decomposition, we implement quantum convolutional neural networks for two complementary scenarios: (i) reverse delegated training, where encrypted data from multiple providers trains a user's network via federated aggregation; (ii) private inference, where users process encrypted data with remote quantum networks. Moreover, analysis of server circuit privacy reveals probabilistic model protection through Pauli gate concealment. These results establish perfectly-secure QHE as a practical framework for multi-party quantum machine learning.

翻译：量子机器学习在云环境中需要保护敏感数据的同时实现远程计算。本文首次展示了完美安全量子同态加密方案在量子神经网络中的实际应用。通过高效的Clifford+$T$门分解，我们实现了适用于两种互补场景的量子卷积神经网络：(i) 反向委托训练——多个数据提供方的加密数据通过联邦聚合方式训练用户网络；(ii) 隐私推理——用户利用远程量子网络处理加密数据。此外，服务器电路隐私分析揭示了通过泡利门隐藏实现的概率模型保护机制。这些成果确立了完美安全量子同态加密作为多方量子机器学习的实用框架。