Quantum Adversarial Machine Learning: Status, Challenges and Perspectives
Demarcus Edwards, Danda B. Rawat
Abstract
Quantum adversarial machine learning Is regarded as a promising approach for studying vulnerabilities of machine learning approaches in adversarial settings and developing defense solutions for adversarial inputs and manipulations in quantum systems. In this paper, we present a current status, proposed approaches and challenges in quantum adversarial machine learning by concentrating on the problems and proposed solutions. We also outline the anticipated problems and perspectives for quantum-assisted machine learning in Near-term quantum computers and limitation in datasets, applications and adversarial examples. With this article, we hope that the readers can have a more thorough understanding of quantum adversarial machine learning and the research trends in this area.