Over the past decades, cyber attackers have become increasingly skilled at compromising systems and circumventing security measures. As a result, detecting and accurately identifying malware is a pressing challenge for many businesses and individuals worldwide.

Cyber-security experts have recently been exploring the potential of machine learning techniques for classifying malware and determining what actions should be taken to eradicate it. While some of these techniques achieved promising results, studies showed that many of them can be fooled or fail to accurately identify malware that they never encountered before.

In the hope of identifying more reliable methods to classify malware, researchers at Orange Innovation Inc. recently carried out a study assessing the potential of the quantum version of machine learning algorithms. Their paper, pre-published on arXiv, offers some initial insight into the strengths and limitations of two types of quantum machine learning models, outlining directions that could be explored in future cyber-security research.

“I have been working on using artificial intelligence for malware analysis since 2019,” Tony Quertier, co-author of the paper, told Tech Xplore. “With Grégoire Barrué, who started his post-doc in October, we want to explore what quantum technology can bring to this problem. As we both have a mathematical background in two complementary areas, we hope to be able to take advantage of our theoretical knowledge to understand this subject.”

Quertier and Barrué believe that quantum machine learning could allow users to extract more information from less data. To test this hypothesis in the context of malware classification, they so far assessed the performance of t