Machine learning meets quantum matter

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 discuss a series of recent developments related to the adaptation of machine learning ideas for the purpose of advancing research in quantum matter, including our works on algorithms that recognize phases of matter, representations of quantum states in terms of neural networks and their applications to the simulation of quantum phases of matter and benchmarking of quantum devices. I will also discuss future directions in areas at the intersection between machine learning and quantum many-body physics.