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.
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Speaker
Larry Liebovitch, Ph.D.
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talks
Professor Emeritus; Adjunct Research Scholar
City University of New York; Climate School, Columbia University
Larry Liebovitch, Ph.D., Professor Emeritus at the City University of New York and Adjunct Research Scholar at Columbia University's Climate School. He studies complex systems with many interacting pieces and uses machine learning and artificial intelligence to study peace.
Bio from: November 12th, 2025 NYC Quantum Computing In Person Meetup (w/teams option)
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