Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption
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Quantum Physics arXiv:2602.12712 (quant-ph) [Submitted on 13 Feb 2026] Title:Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption Authors:Sergio A. Ortega, Miguel A. Martin-Delgado View a PDF of the paper titled Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption, by Sergio A. Ortega and 1 other authors View PDF 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. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.12712 [quant-ph] (or arXiv:2602.12712v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.12712 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sergio A. Ortega [view email] [v1] Fri, 13 Feb 2026 08:27:39 UTC (1,589 KB) Full-text links: Access Paper: View a PDF of the paper titled Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption, by Sergio A. Ortega and 1 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-02 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)