Title: Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks,
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.