Theoretical Nanophysics
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Solving Quantum Many-Body Problem with Feed-Forward Neural Networks

ShengHsuan Lin, LMU

13.04.2018 at 09:00 

Motivated by the success of describing quantum states with restricted Boltzmann machine, we consider general feed-forward neural networks as variational wavefunctions. We show that with variational Monte Carlo method or with supervised learning, neural networks could be trained to represent quantum states. We demonstrated the ability of neural network representing quantum states on frustrated problems including 1d and 2d J1-J2 model. The difficulties of optimization large number of parameters are addressed. Considering the connection between natural gradient method and stochastic reconfiguration method, we point out possible way to extend the result to deep networks with large number of parameters.

A 450, Theresienstr. 37