Applications of machine learning to quantum physics
This article is about classical machine learning of quantum systems. For machine learning enhanced by quantum computation, see quantum machine learning.
Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement.[1] Other examples include learning Hamiltonians,[2][3] learning quantum phase transitions,[4][5] and automatically generating new quantum experiments.[6][7][8][9] Classical machine learning is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including quantum information theory, quantum technologies development, and computational materials design. In this context, it can be used for example as a tool to interpolate pre-calculated interatomic potentials[10] or directly solving the Schrödinger equation with a variational method.[11]