%0 Generic
%A Ortega, Sergio A.
%A Martín-Delgado Alcántara, Miguel Ángel
%T Reverse delegated training and private inference via perfectly-secure quantum homomorphic encryption
%D 2026
%U https://hdl.handle.net/20.500.14352/137282
%X 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.
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