Machine learning for quantum matter
A short review on the recent development and adaptation of machine learning ideas for the purpose of advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems.
Abstract
Quantum matter, the research field studying phases of matter whose properties\nare intrinsically quantum mechanical, draws from areas as diverse as hard\ncondensed matter physics, materials science, statistical mechanics, quantum\ninformation, quantum gravity, and large-scale numerical simulations. Recently,\nresearchers interested quantum matter and strongly correlated quantum systems\nhave turned their attention to the algorithms underlying modern machine\nlearning with an eye on making progress in their fields. Here we provide a\nshort review on the recent development and adaptation of machine learning ideas\nfor the purpose advancing research in quantum matter, including ideas ranging\nfrom algorithms that recognize conventional and topological states of matter in\nsynthetic an experimental data, to representations of quantum states in terms\nof neural networks and their applications to the simulation and control of\nquantum systems. We discuss the outlook for future developments in areas at the\nintersection between machine learning and quantum many-body physics.\n