Machine learning for quantum matter

• Main Author: Juan Carrasquilla
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: Advances in Physics: X
• Subjects: strongly correlated quantum systems; machine learning
• Online Access: http://dx.doi.org/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.