Method to solve quantum few-body problems with artificial neural networks
Disordered Systems and Neural Networks
2018-08-01 v1 Quantum Gases
Computational Physics
Abstract
A machine learning technique to obtain the ground states of quantum few-body systems using artificial neural networks is developed. Bosons in continuous space are considered and a neural network is optimized in such a way that when particle positions are input into the network, the ground-state wave function is output from the network. The method is applied to the Calogero-Sutherland model in one-dimensional space and Efimov bound states in three-dimensional space.
Cite
@article{arxiv.1804.06521,
title = {Method to solve quantum few-body problems with artificial neural networks},
author = {Hiroki Saito},
journal= {arXiv preprint arXiv:1804.06521},
year = {2018}
}
Comments
7 pages, 5 figures