Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks
Quantum Physics
2018-09-17 v2 Disordered Systems and Neural Networks
Computational Physics
Abstract
Inspired by the recent work of Carleo and Troyer[1], we apply machine learning methods to quantum mechanics in this article. The radial basis function network in a discrete basis is used as the variational wavefunction for the ground state of a quantum system. Variational Monte Carlo(VMC) calculations are carried out for some simple Hamiltonians. The results are in good agreements with theoretical values. The smallest eigenvalue of a Hermitian matrix can also be acquired using VMC calculations. Our results demonstrate that machine learning techniques are capable of solving quantum mechanical problems.
Cite
@article{arxiv.1710.03213,
title = {Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks},
author = {Peiyuan Teng},
journal= {arXiv preprint arXiv:1710.03213},
year = {2018}
}