Quantum-Enhanced Adversarial Robustness in Artificial Intelligence

2026-05-27

AI Summary

Artificial intelligence models deployed in critical domains such as healthcare, finance, and autonomous driving are vulnerable to adversarial attacks, which compromise their reliability and safety. This work presents the first systematic theoretical framework for quantum-enhanced adversarial robustness, introducing a novel approach that integrates quantum optimization, quantum feature mapping, and hybrid quantum-classical neural networks. By harnessing fundamental quantum mechanisms—including superposition, entanglement, and interference—the proposed method significantly strengthens model resilience against adversarial perturbations. The study establishes a new paradigm for developing secure and trustworthy AI systems and demonstrates the unique potential of quantum technologies in enhancing the robustness of artificial intelligence.

Abstract

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.

Problem

• adversarial robustness
• artificial intelligence
• quantum computing
• adversarial attacks
• trustworthy AI

Innovation

• quantum-enhanced robustness
• adversarial machine learning
• quantum optimization
• quantum feature mapping
• hybrid quantum-classical architectures