Machine learning algorithms have been widely used for various tasks such as image classification and features detection. However, their vulnerability to adversarial examples — maliciously crafted inputs created to fool the algorithm — remains a major issue. The integration of machine learning with quantum computing might provide an opportunity for effective tools that offer greater accuracy, faster compute times, and robustness against adversarial attacks. Recent breakthroughs in quantum-mechanical research have enabled the development of quantum adversarial machine learning (QAML), opening the door to potential new sources of quantum advantage. QAML has already yielded positive results, but there are still challenges to creating practical tools. In this perspective, we discuss recent developments in the field, outline the most important hurdles, and suggest potential future paths.
Nature Machine Intelligence is a research company specializing in the development of quantum-related technologies in the fields of machine learning and Artificial Intelligence (AI). It is a core mover in the field of quantum adversarial machine learning (QAML). The company’s team of experts is continuously exploring new ideas related to quantum computing, machine learning, and AI to ensure that they remain on the cutting edge of such fields. They have been researching the possibilities of finding a quantum advantage in machine learning algorithms, striving to make QAML practical for real-world use.
A key player in the field of quantum adversarial machine learning is Professor Giacomo De Palma. He is a renowned professor and researcher in the Department of Physics and Astronomy at the University of Padua, and a leading scholar in the field of quantum computing. He has made key contributions to quantum machine learning, and to the development of quantum-enhanced adversarial robustness. Professor De Palma has pioneered numerous research projects and publications related to quantum machine learning, and has been a significant contributor to the RisingStars Qarma ERC Project. With his expertise, he is leading the way in developing future practical applications of QAML.
