Nonlinear Network description for many-body quantum systems in continuous space
Abstract
We show that the recently introduced iterative backflow renormalization can be interpreted as a general neural network in continuum space with non-linear functions in the hidden units. We use this wave function within Variational Monte Carlo for liquid He in two and three dimensions, where we typically find a tenfold increase in accuracy over currently used wave functions. Furthermore, subsequent stages of the iteration procedure define a set of increasingly good wave functions, each with its own variational energy and variance of the local energy: extrapolation of these energies to zero variance gives values in close agreement with the exact values. For two dimensional He, we also show that the iterative backflow wave function can describe both the liquid and the solid phase with the same functional form -a feature shared with the Shadow Wave Function, but now joined by much higher accuracy. We also achieve significant progress for liquid He in three dimensions, improving previous variational and fixed-node energies for this very challenging fermionic system.
Cite
@article{arxiv.1711.01993,
title = {Nonlinear Network description for many-body quantum systems in continuous space},
author = {Michele Ruggeri and Saverio Moroni and Markus Holzmann},
journal= {arXiv preprint arXiv:1711.01993},
year = {2018}
}