41 citations · 56 across the 4 of their papers we have counts for
4 papers
Probing -wave pairing in the -- Hubbard model with tensor-backflow wave functions
Xiao Liang
Building on the recently developed tensor-backflow method, which accurately describes energies and stripe states in the Fermi-Hubbard model, we investigate -wave pairing in the…
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.…
Hybrid convolutional neural network and PEPS wave functions for quantum many-particle states
Xiao Liang, Shao-Jun Dong, Lixin He
Neural networks have been used as variational wave functions for quantum many-particle problems. It has been shown that the correct sign structure is crucial to obtain the high acc…