Overview: Machine Learning for Quantum Matter Research | AISC

Speaker(s): Juan Felipe Carrasquilla Alvarez
Host: Amir Feizpour

Motivation / Abstract
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, and large-scale numerical simulations. Recently, researchers interested in condensed matter physics have turned their attention to the algorithms underlying modern machine learning, with an eye on making progress in their fields. In this talk I will informally discuss a series of recent developments related to the adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize phases of matter in synthetic an experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. I will also discuss the outlook for future developments in areas at the intersection between machine learning and quantum physics.

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