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 feedforward neural networks as a general purpose trial wave function for quantum Monte Carlo simulations of continous manybody systems. Whereas for simple model systems the whole manybody wave function can be represented by a neural network, the antisymmetry condition of nontrivial 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.
 Publication:

arXiv eprints
 Pub Date:
 April 2019
 arXiv:
 arXiv:1904.10251
 Bibcode:
 2019arXiv190410251K
 Keywords:

 Physics  Computational Physics
 EPrint:
 Bump to the submitted&