Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
Satwik Kundu, Swaroop Ghosh
Abstract
In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.
Topics & Concepts
ExploitComputer scienceDomain (mathematical analysis)Computer securityQuantum computerTheoretical computer scienceQuantumSpace (punctuation)Artificial intelligenceMathematicsOperating systemQuantum mechanicsPhysicsMathematical analysisQuantum Computing Algorithms and ArchitectureCryptography and Data SecurityBlockchain Technology Applications and Security