Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption

• URL: http://arxiv.org/abs/2602.12712v1
• Date: Fri, 13 Feb 2026 08:27:39 GMT
• Title: Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption
• Authors: Sergio A. Ortega, Miguel A. Martin-Delgado,
• Abstract summary: We show first realistic implementations of a perfectly-secure quantum homomorphic encryption scheme applied to quantum neural networks (QNN)<n>Results establish perfectly-secure QHE as a practical framework for multi-party quantum machine learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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