Ortega, Sergio A.Martín-Delgado Alcántara, Miguel Ángel2026-06-092026-06-092026https://hdl.handle.net/20.500.14352/137282Dataset de: "Ortega, S. A., & Martin-Delgado, M. A. (2026). Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption. arXiv preprint arXiv:2602.12712"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.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/datasethttps://github.com/OrtegaSA/CQC-QHE-repohttps://github.com/OrtegaSA/qnn-cqcopen access530.145004Quantum machine learningQuantum neural networksQuantum homomorphic encryption.Física (Física)22 Física