Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo
Abstract
Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, we propose feed-forward neural networks as a general purpose trial wave function for quantum Monte Carlo simulations of continous many-body systems. Whereas for simple model systems the whole many-body wave function can be represented by a neural network, the antisymmetry condition of non-trivial fermionic systems is incorporated by means of a Slater determinant. To demonstrate the accuracy of our trial wave functions, we have studied an exactly solvable model system of two trapped interacting particles, as well as the hydrogen dimer.
Keywords
Cite
@article{arxiv.1904.10251,
title = {Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo},
author = {Jan Kessler and Francesco Calcavecchia and Thomas D. Kühne},
journal= {arXiv preprint arXiv:1904.10251},
year = {2021}
}
Comments
Bump to the submitted&accepted version. Many relatively minor changes from previous upload, due to the peer reviewing process