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  4. Predominant Aspects on Security for Quantum Machine Learning: Literature Review
 
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2024
Conference Paper
Title

Predominant Aspects on Security for Quantum Machine Learning: Literature Review

Abstract
Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML's reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QML in real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.
Author(s)
Franco, Nicola  
Fraunhofer-Institut für Kognitive Systeme IKS  
Sakhnenko, Alona
Fraunhofer-Institut für Kognitive Systeme IKS  
Stolpmann, Leon
adesso
Thuerck, Daniel
Quantagonia
Petsch, Fabian
Bundesamt für Sicherheit in der Informationstechnik -BSI-, Bonn  
Rüll, Annika
Bundesamt für Sicherheit in der Informationstechnik -BSI-, Bonn  
Lorenz, Jeanette Miriam  orcid-logo
Fraunhofer-Institut für Kognitive Systeme IKS  
Mainwork
IEEE Quantum Week 2024. Proceedings. Volume III: Third IEEE Quantum Science and Engineering Education Conference, QSEEC 2024  
Conference
Quantum Science and Engineering Education Conference 2024  
Quantum Week 2024  
Open Access
DOI
10.1109/QCE60285.2024.00173
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • security

  • quantum machine learning

  • quantum computing

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