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Approximating Quantum Many-Body Wave Functions using Artificial Neural Networks

Speaker

Zi Cai, School of Physics and Astronomy, Shanghai Jiao Tong University

Time

2020.05.21 10:30-11:30

Venue

Online—ZOOM APP

ZOOM Info

Zoom ID: 811-714-09349
PIN Code: 379208

Abstract

In this talk, we demonstrate the expressibility of artificial neural networks (ANNs) in quantum many-body physics by showing that a feed-forward neural network with a small number of hidden layers can be trained to approximate with high precision the ground states of some notable quantum many-body systems. We consider the one-dimensional free bosons and fermions, spinless fermions on a square lattice away from half-filling, as well as frustrated quantum magnetism with a rapidly oscillating ground-state characteristic function. In the latter case, an ANN with a standard architecture fails, while that with a slightly modified one successfully learns the frustration-induced complex sign rule in the ground state and approximates the ground states with high precisions. As an example of practical use of our method, we also perform the variational method to explore the ground state of an antiferromagnetic J1-J2 Heisenberg model.