Artificial Intelligence (AI) and Machine Learning (ML) are becoming the primary approaches to solving universal problems across several areas.
In parallel, the capabilities of quantum technology are expanding rapidly. Together, they are meeting to create quantum AI and ML, specifically new quantum-based AI and ML tools. Indeed, future mature quantum computers will grant an edge to their adopters. That’s undeniable. We present concepts for advanced AI and ML architectures that integrate principles from quantum computing, AI, and ML. Using case studies, we demonstrate how to leverage quantum resources to solve problems in AI and ML. We will address the question: how to make quantum AI and ML competitive with classical AI and ML.
BIO :
Michel Barbeau is a professor of Computer Science. He got a Ph.D. in Computer Science (Université de Montréal, Canada, Canada’91. From ’91 to ’99, he was a professor at Université de Sherbrooke. During the ’98-’99 academic year, he was a visiting researcher at the University of Aizu, Japan. Since 2000, he has worked at Carleton University, School of Computer Science, Canada. Michel Barbeau primary area of expertise is computer networks, specifically architecture and protocols. Research interests include quantum computing, underwater communications and networks, drones, quantum algorithms, and network control systems. Michel Barbeau has numerous publications in the field of AI and quantum computing. He has contributed to solving problems by leveraging knowledge graph representations, graph analytics, and machine learning. He has demonstrated that the fusion of robotics and quantum computing enables optimization-based approaches. Michel Barbeau is a member of the NATO Science & Technology Organization (STO) Exploratory Team TSI?ET?008: Enabling NATO Resilience Through Quantum?Secure Technologies and a member of the QuARC (Quantum Advancement Research Centre) at Carleton University.