To solve relevant problems in science, engineering, and business, computers were first programmed with the explicit instructions needed to solve those problems. Now, machine learning and AI have shown that it is more powerful to first train computers to learn and then give them the data to solve those problems. These new methods are now being applied to train quantum computers. I will review successes and limitations in training: 1) full quantum computers using only Dirac operator gates, 2) hybrid conventional-quantum computers using variational quantum circuits, and 3) quantum annealing computers using equilibrium propagation, the quantum analog to back propagation.
Speaker: Larry S, Liebovitch, Professor emeritus City University of New York and Adjunct Research Scholar in the Advanced Consortium on Cooperation, Conflict and Complexity in the Climate School at Columbia University.
Dr Liebovitch studies complex systems with many interacting pieces in the physical, biological, and social sciences and uses machine learning and artificial intelligence to study peace. He earned a BS in Physics from The City College of New York and a PhD in Astronomy from Harvard University. He has held faculty positions in Departments of Physics, Psychology, and Ophthalmology and served as the Acting Director of the Florida Atlantic University Center for Complex Systems and Brain Sciences and as the Dean of the Division of Mathematics and Natural Sciences of Queens College, City University of New York.
