November 12th, 2025 NYC Quantum Computing In Person Meetup (w/teams option)

Title: Machine Learning on Quantum Computers

Abstract: To solve problems in science, engineering, and business, computers were first programmed with the explicit instructions to solve those problems. Now, AI has shown that it is more powerful to first train computers to learn and then give them the data needed to solve a problem. I will review the successes and limitations of machine learning methods being used to train: 1) quantum computers with only Dirac operator gates, 2) hybrid classical-quantum computers with variational quantum circuits, 3) quantum Hopfield computers using equilibrium propagation, a quantum replacement for back propagation, and 4) quantum computer annealers.

Bio: 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.

Larry Liebovitch, Ph.D.
email: [liebovitch@gmail.com](mailto:liebovitch@gmail.com)
web: https://sites.google.com/view/Larry-phd
linkedin: https://www.linkedin.com/in/larry-liebovitch-9967255a
youtube: https://www.youtube.com/@LarryPhD-rk2qf