72 citations · 90 across the 5 of their papers we have counts for
5 papers
Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method
Xiao Liang, Mingfan Li, Qian Xiao +12
For decades, people are developing efficient numerical methods for solving the challenging quantum many-body problem, whose Hilbert space grows exponentially with the size of the p…
Bridging the Gap between Deep Learning and Frustrated Quantum Spin System for Extreme-scale Simulations on New Generation of Sunway Supercomputer
Mingfan Li, Junshi Chen, Qian Xiao +7
Efficient numerical methods are promising tools for delivering unique insights into the fascinating properties of physics, such as the highly frustrated quantum many-body systems.…
Reply to comments on "Gapless spin liquid ground state of spin-1/2 - Heisenberg model on square lattices"
Wen-Yuan Liu, Shaojun Dong, Chao Wang +4
In a recent comments [arXiv:1909.12788 (2019)], Zhao et al. argue that the definition of dimer orders used in our paper [Phys. Rev. B 98, 241109 (2018)] may not rule out valence bo…
Gapless spin liquid ground state of spin-1/2 - Heisenberg model on square lattices
Wen-Yuan Liu, Shaojun Dong, Chao Wang +4
The spin-1/2 - Heisenberg model on square lattices are investigated via the finite projected entangled pair states (PEPS) method. Using the recently developed gradient op…
PEPS++: Towards Extreme-scale Simulations of Strongly Correlated Quantum Many-particle Models on Sunway TainhuLight
Lixin He, Hong An, Chao Yang +10
The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for…