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What ML Can Do For Quantum Many-body Physics
Juan Carrasquilla

Mon Dec 06 08:00 AM -- 08:43 AM (PST) @
in Machine Learning With Quantum Computers »

The last section is a little more “physics” themed, and shows how the connection between machine learning models and quantum states works the other way around: we can use neural networks as an ansatz to compress and learn our description of quantum systems.

Author Information

Juan Carrasquilla (Vector Institute)

Juan Carrasquilla is a full-time researcher at the Vector Institute for Artificial Intelligence in Toronto, Canada, where he works on the intersection of condensed matter physics, quantum computing, and machine learning - such as combining quantum Monte Carlo simulations and machine learning techniques to analyze the collective behaviour of quantum many-body systems. He completed his PhD in Physics at the International School for Advanced Studies in Italy and has since held positions as a Postdoctoral Fellow at Georgetown University and the Perimeter Institute, as a Visiting Research Scholar at Penn State University, and was a Research Scientist at D-Wave Systems Inc. in Burnaby, British Columbia.

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