Bryan Clark, Assistant Professor from the Grainger College of Engineering at the University of Illinois at Urbana-Champaign, will present "Machine Learning for Quantum Computing and Quantum Matter" on Monday, October 5 at 11:00 a.m.
Abstract: Simulating quantum systems, from quantum materials to quantum circuits, is computationally costly; in fact, this is the heart of why quantum computers are powerful. A single quantum state is a high-dimensional object containing an exponentially large amount of information. Machine learning architectures are well suited to compress high-dimensional data, especially probability distributions. Unfortunately quantum states have both positive and negative amplitudes. In this talk, we describe how we are using machine learning to improve the classical simulation of quantum materials and quantum computers with a particular focus on dealing with states with varying signs.
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Seminar Zoom link.