A Real Neural Network State for Quantum Chemistry
Yangjun Wu, Xiansong Xu, Dario Poletti, Yi Fan, Chu Guo, Honghui Shang
Abstract
The restricted Boltzmann machine (RBM) has recently been demonstrated as a useful tool to solve the quantum many-body problems. In this work we propose tanh-FCN, which is a single-layer fully connected neural network adapted from RBM, to study ab initio quantum chemistry problems. Our contribution is two-fold: (1) our neural network only uses real numbers to represent the real electronic wave function, while we obtain comparable precision to RBM for various prototypical molecules; (2) we show that the knowledge of the Hartree-Fock reference state can be used to systematically accelerate the convergence of the variational Monte Carlo algorithm as well as to increase the precision of the final energy.