Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
For researchers in quantum machine learning and adversarial robustness, this survey consolidates knowledge on a nascent field, though it is primarily a literature review without novel contributions.
This survey reviews the emerging field of quantum adversarial machine learning, covering vulnerabilities of quantum machine learning models, possible attacks, and defense strategies. It provides a comprehensive overview of existing methods and theoretical foundations.
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.