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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.12712 |
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| _version_ | 1866908837209964544 |
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| author | Ortega, Sergio A. Martin-Delgado, Miguel A. |
| author_facet | Ortega, Sergio A. Martin-Delgado, Miguel A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12712 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption Ortega, Sergio A. Martin-Delgado, Miguel A. Quantum Physics Neural and Evolutionary Computing 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. |
| title | Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption |
| topic | Quantum Physics Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2602.12712 |