4 papers
Empowering Neural Network-based Quantum Monte Carlo with Local Pseudopotentials
Weizhong Fu, Ryunosuke Fujimaru, Ruichen Li +9
Neural Network-based Quantum Monte Carlo (NNQMC), an emerging method for solving many-body quantum systems with high accuracy, has been mainly applied to small systems due to deman…
Deep Learning Sheds Light on Integer and Fractional Topological Insulators
Xiang Li, Yixiao Chen, Bohao Li +4
Electronic topological phases of matter, characterized by robust boundary states derived from topologically nontrivial bulk states, are pivotal for next-generation electronic devic…
Taming Landau level mixing in fractional quantum Hall states with deep learning
Yubing Qian, Tongzhou Zhao, Jianxiao Zhang +3
Strong correlation brings a rich array of emergent phenomena, as well as a daunting challenge to theoretical physics study. In condensed matter physics, the fractional quantum Hall…
Probing quantum critical phase from neural network wavefunction
Haoxiang Chen, Weiluo Ren, Xiang Li +1
One-dimensional (1D) systems and models provide a versatile platform for emergent phenomena induced by strong electron correlation. In this work, we extend the newly developed real…