https://doi.org/10.1140/epjp/s13360-024-05311-y
Review
Neural-network quantum states for many-body physics
1
Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, 10010, New York, NY, USA
2
Department of Physics, Columbia University, 10027, New York, NY, USA
3
Department of Physics, Center for Quantum Phenomena, New York University, 726 Broadway, 10003, New York, NY, USA
4
IBM Quantum, IBM Research, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA
a
matija.medvidovic@columbia.edu
Received:
14
March
2024
Accepted:
24
May
2024
Published online:
19
July
2024
Variational quantum calculations have borrowed many tools and algorithms from the machine learning community in the recent years. Leveraging great expressive power and efficient gradient-based optimization, researchers have shown that trial states inspired by deep learning problems can accurately model many-body correlated phenomena in spin, fermionic and qubit systems. In this review, we derive the central equations of different flavors variational Monte Carlo (VMC) approaches, including ground state search, time evolution and overlap optimization, and discuss data-driven tasks like quantum state tomography. An emphasis is put on the geometry of the variational manifold as well as bottlenecks in practical implementations. An overview of recent results of first-principles ground-state and real-time calculations is provided.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
