University of Minnesota
School of Physics & Astronomy

CM Journal Club

Wednesday, October 25th 2017
4:30 pm:
CM Journal Club in Tate 201-20
Speaker: Ruiqi Xing
Subject: Machine Learning circumvents sign problem in Quantum Monte Carlo

The journal club talk will be about the paper [1]. I will first introduce the famous fermion sign problem [2] in Determinantal Quantum Monte Carlo(DQMC) [3], and then discuss how to circumvent it using a method developed in machine learning community, convolutional neural networks. Introduction to neural networks [4] will be given. In the end, A successful application of this method to distinguish phases and to identify quantum phase transitions will be presented.

[1]Machine learning quantum phases of matter beyond the fermion sign problem
[2]Sign problem in the numerical simulation of many-electron systems
[3]Monte Carlo calculations of coupled boson-fermion systems. I
[4]Neural Networks and Deep Learning

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