Neural-Network Quantum States: A Systematic Review
Abstract
The so-called contemporary AI revolution has reached every corner of the social, human and natural sciences -- physics included. In the context of quantum many-body physics, its intersection with machine learning has configured a high-impact interdisciplinary field of study; with the arise of recent seminal contributions that have derived in a large number of publications. One particular research line of such field of study is the so-called Neural-Network Quantum States, a powerful variational computational methodology for the solution of quantum many-body systems that has proven to compete with well-established, traditional formalisms. Here, a systematic review of literature regarding Neural-Network Quantum States is presented.
Cite
@article{arxiv.2204.12966,
title = {Neural-Network Quantum States: A Systematic Review},
author = {David R. Vivas and Javier Madroñero and Victor Bucheli and Luis O. Gómez and John H. Reina},
journal= {arXiv preprint arXiv:2204.12966},
year = {2022}
}
Comments
9 pages, 4 figures