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.
@article{arxiv.2602.12712,
title = {Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption},
author = {Sergio A. Ortega and Miguel A. Martin-Delgado},
journal= {arXiv preprint arXiv:2602.12712},
year = {2026}
}