Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods

2026-05-12

AI Summary

Quantum machine learning models are vulnerable to adversarial attacks and lack systematic defense mechanisms. This work presents the first comprehensive survey of quantum adversarial machine learning, clearly distinguishing between classically transferred and quantum-native approaches. It systematically organizes existing attack strategies, defense techniques, and underlying theoretical foundations. By integrating insights from quantum computing, classical and quantum machine learning, and adversarial robustness research, the paper establishes a unified conceptual framework for the field. This synthesis elucidates key challenges and outlines promising future directions, offering both theoretical grounding and a strategic roadmap for developing secure and reliable quantum intelligent systems.

Abstract

Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine learning is a field that studies these vulnerabilities to build robust machine learning models. Quantum machine learning is an interdisciplinary field that bridges quantum computing and classical machine learning. While quantum machine learning shows potentials to outperform classical machine learning in complex tasks such as regression, classification, and generative modeling, it remains vulnerable to adversarial attacks. Given the recent advancements in quantum computing and machine learning, the quantum adversarial machine learning field has emerged to study the vulnerabilities of quantum machine learning, possible attacks, and novel quantum-enhanced defense strategies. In this survey, we provide a detailed overview on quantum adversarial machine learning and explore the existing attacks and countermeasures. We also review the theoretical underpinnings of this area, emerging trends, and critical challenges.

Problem

• Quantum Adversarial Machine Learning
• Adversarial Attacks
• Quantum Machine Learning
• Model Robustness
• Security Vulnerabilities

Innovation

• Quantum Adversarial Machine Learning
• Quantum-Native Methods
• Adversarial Attacks
• Quantum Machine Learning
• Robustness