Subject Review: AI-Driven Security in Quantum Machine Learning:Vulnerabilities, Threats, and Defenses

Authors

  • Farah Neamah Abbas College of Education, Mustansiriyah University Baghdad, Iraq
  • Mohanad Ridha Ghanim College of Education, Mustansiriyah University Baghdad, Iraq
  • Rafal Naser Saleh College of Education, Mustansiriyah University Baghdad, Iraq

DOI:

https://doi.org/10.31695/IJERAT.2025.4.1

Keywords:

Adversarial Attacks in QML, Quantum Security Frameworks, Quantum Error Correction (QEC), Quantum Machine Learning (QML), Post-Quantum Cryptography (PQC), Quantum Homomorphic Encryption (QHE)

Abstract

Quantum Machine Learning (QML) has advanced significantly thanks to the combination of Quantum Computing (QC) with Artificial Intelligence (AI), hence releasing computational benefits over conventional methods. This synergy does, however, also bring fresh security flaws like adversarial attacks, quantum noise manipulation, and cryptographic weaknesses. This work offers a thorough investigation of QML security along with an examination of its special vulnerabilities resulting from hardware-induced faults, quantum variational circuits, and quantum data encoding. We methodically investigate adversarial attack techniques using the probabilistic character of quantum states including side-channel assaults, quantum noise injection, and algorithmic perturbations. We also assess innovative defensive strategies such differential privacy, quantum adversarial training, quantum error correction (QEC), cryptographic techniques include Quantum Homomorphic Encryption (QHE), We offer a hybrid AI-driven method for protecting QML models against developing threats by linking artificial intelligence and quantum security frameworks. This work emphasizes the importance of developing quantum-safe AI systems and consistent adversarial robustness standards. The results help to advance AI-enhanced quantum security, thereby guaranteeing the future of QML applications is efficient, strong, and resistant to adversarial attack.

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Published

2025-04-25

Issue

Section

Review Article

How to Cite

Subject Review: AI-Driven Security in Quantum Machine Learning:Vulnerabilities, Threats, and Defenses. (2025). International Journal of Engineering Research and Advanced Technology (ijerat) (E-ISSN 2454-6135) , 11(4), 1-22. https://doi.org/10.31695/IJERAT.2025.4.1