RT Generic
T1 Reverse delegated training and private inference via perfectly-secure quantum homomorphic encryption
A1 Ortega, Sergio A.
A1 Martín-Delgado Alcántara, Miguel Ángel
AB 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.
PB GitHub
YR 2026
FD 2026
LK https://hdl.handle.net/20.500.14352/137282
UL https://hdl.handle.net/20.500.14352/137282
LA eng
NO Dataset 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"
DS Docta Complutense
RD 7 nov 2026