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Title: Machine Learning on Quantum Computers
Abstract: To solve problems in science, engineering, & 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 & then give them the data needed to solve a problem. I will review the successes & 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, & 4) quantum computer annealers.
Bio: Larry S, Liebovitch, Professor emeritus City University of New York & Adjunct Research Scholar in the Advanced Consortium on Cooperation, Conflict & Complexity in the Climate School at Columbia University. Dr Liebovitch studies complex systems with many interacting pieces in the physical, biological, & social sciences & uses machine learning & artificial intelligence to study peace. He earned a BS in Physics from The City College of New York & a PhD in Astronomy from Harvard University. He has held faculty positions in Departments of Physics, Psychology, & Ophthalmology & served as the Acting Director of the Florida Atlantic University Center for Complex Systems & Brain Sciences & as the Dean of the Division of Mathematics & 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
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