AUTHOR=Nordhagen Even M. , Kim Jane M. , Fore Bryce , Lovato Alessandro , Hjorth-Jensen Morten 

TITLE=Efficient solutions of fermionic systems using artificial neural networks

JOURNAL=Frontiers in Physics

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1061580

DOI=10.3389/fphy.2023.1061580

ISSN=2296-424X

ABSTRACT=We discuss differences and similarities between variational Monte
  Carlo approaches that use conventional and artificial neural network
  parameterizations of the ground-state wave function for systems of
  fermions. We focus on a relatively shallow neural-network
  architectures, the so called restricted Boltzmann machine, and
  discuss unsupervised learning algorithms that are suitable to model
  complicated many-body correlations. We analyze the strengths and
  weaknesses of conventional and neural-network wave functions by
  solving various circular quantum-dots systems. Results for up to 90
  electrons are presented and particular emphasis is placed on how to
  efficiently implement these methods on homogeneous and heterogeneous
  high-performance computing facilities.