Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities

[2412.12373] Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2412.12373 (quant-ph) [Submitted on 16 Dec 2024] Title: Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities Authors: Eric Yocam , Anthony Rizi , Mahesh Kamepalli , Varghese Vaidyan , Yong Wang , Gurcan Comert View a PDF of the paper titled Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities, by Eric Yocam and 5 other authors View PDF HTML (experimental) Abstract: As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and deployment. These principles are supported by various quantum strategies, including quantum data anonymization, quantum-resistant neural networks, and quantum encryption. The paper also identifies open issues in quantum security, privacy, and trust, and recommends exploring adaptive adversarial attacks and auto adversarial attacks as future directions. The proposed design principles and recommendations provide guidance for developing quantum-secure neural networks, ensuring the integrity and reliability of machine learning models in the quantum era. Comments: 24 pages, 9 figures, 12 tables Subjects: Quantum Physics (quant-ph) ; Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2412.12373 [quant-ph] (or arXiv:2412.12373v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2412.12373 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Quantum Robustness in Artificial Intelligence, Quantum Science and Technology, pp. 55-89, Springer, Cham, 2026 Related DOI : https://doi.org/10.1007/978-3-032-11153-1_4 Focus to learn more DOI(s) linking to related resources Submission history From: Eric Yocam [ view email ] [v1] Mon, 16 Dec 2024 22:07:27 UTC (426 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities, by Eric Yocam and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2024-12 Change to browse by: cs cs.CR cs.LG References & Citations INSPIRE HEP NASA ADS Google 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? ) 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? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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