5 papers
Locality-Induced Hierarchical Backflow Wavefunctions for Correlated Fermions
Yu-Tong Zhou, Zheng-Wei Zhou, Wen-Yuan Liu
We show that locality provides a natural principle to hierarchically organize backflow wavefunctions. This leads us to propose a family of variational fermionic states, termed hier…
Fermionic tensor network contraction for arbitrary geometries
Yang Gao, Huanchen Zhai, Johnnie Gray +5
We describe our implementation of fermionic tensor network contraction on arbitrary lattices within both a globally ordered and locally ordered formalism. We provide a pedagogical…
Accurate Gauge-Invariant Tensor Network Simulations for Abelian Lattice Gauge Theory in (2+1)D: ground state and real-time dynamics
Yantao Wu, Wen-Yuan Liu
We propose a novel tensor network method to achieve accurate and efficient simulations of Abelian lattice gauge theories (LGTs) in (2+1)D for both ground state and real-time dynami…
Accurate Simulation of the Hubbard Model with Finite Fermionic Projected Entangled Pair States
Wen-Yuan Liu, Huanchen Zhai, Ruojing Peng +2
We demonstrate the use of finite-size fermionic projected entangled pair states, in conjunction with variational Monte Carlo, to perform accurate simulations of the ground-state of…
Tensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States
Wen-Yuan Liu, Si-Jing Du, Ruojing Peng +2
We introduce a change of perspective on tensor network states that is defined by the computational graph of the contraction of an amplitude. The resulting class of states, which we…