Machine learning for quantum matter

• Published in: Advances in physics: X Vol. 5; no. 1; p. 1797528
• Main Author: Carrasquilla, Juan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.01.2020; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Computer simulation; Condensed matter physics; Machine learning; Materials science; Neural networks; Quantum gravity; Quantum mechanics; Quantum phenomena; Statistical mechanics; Strongly correlated quantum systems
• ISSN: 2374-6149; 2374-6149
• DOI: 10.1080/23746149.2020.1797528

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, quantum gravity, and large-scale numerical simulations. Recently, researchers interested in quantum matter and strongly correlated quantum systems have turned their attention to the algorithms underlying modern machine learning with an eye on making progress in their fields. Here we provide a short review on the recent development and adaptation of machine learning ideas for the purpose 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. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.