2026-07-26T04:14:38Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1372822026-06-10T00:07:51Zcom_20.500.14352_14col_20.500.14352_16

Ortega, Sergio A. Martín-Delgado Alcántara, Miguel Ángel 2026-06-09T16:36:12Z 2026-06-09T16:36:12Z 2026 https://hdl.handle.net/20.500.14352/137282 https://github.com/OrtegaSA/CQC-QHE-repo https://github.com/OrtegaSA/qnn-cqc 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. eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 International Reverse delegated training and private inference via perfectly-secure quantum homomorphic encryption dataset