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

TL;DR

This paper surveys the emerging field of quantum adversarial machine learning, analyzing vulnerabilities, attacks, defenses, and future challenges in quantum-enhanced models.

Contribution

It provides a comprehensive overview of quantum adversarial machine learning, including existing attacks, countermeasures, and theoretical foundations.

Findings

Quantum machine learning is vulnerable to adversarial attacks.

Quantum-enhanced defenses are being developed against these attacks.

The field faces significant theoretical and practical challenges.

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.