Quantum-Enhanced Adversarial Robustness in Artificial Intelligence

Computer Science > Cryptography and Security [Submitted on 27 May 2026] Quantum-Enhanced Adversarial Robustness in Artificial Intelligence Jaydip Sen Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety critical systems such as healthcare, finance, and autonomous technologies. In parallel, quantum computing has emerged as a transformative paradigm capable of addressing complex computational problems through principles such as superposition, entanglement, and quantum interference. The convergence of these fields has led to the emergence of quantum artificial intelligence, which explores how quantum techniques can enhance learning efficiency, scalability, and robustness. This chapter provides a comprehensive overview of adversarial machine learning and existing defense strategies, followed by an accessible introduction to quantum computing and quantum machine learning models. It further presents conceptual frameworks for quantum-enhanced adversarial robustness, emphasizing quantum optimization, feature mapping, and hybrid quantum classical architectures. Practical applications, key challenges, and future research directions are also discussed to support the development of secure and trustworthy AI systems. Comments: This is the pre-print of the chapter which has been accepted for publication in the edited volume titled "Quantum Enhancements to the AI Industry", edited by Eduard Babulak. The volume will be published by IGI Global, USA. This is not the final version of the chapter published in the book Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2605.28899 [cs.CR]   (or arXiv:2605.28899v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.28899 Focus to learn more Submission history From: Jaydip Sen [view email] [v1] Wed, 27 May 2026 14:51:50 UTC (1,209 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)