16 citations · 46 across the 9 of their papers we have counts for
8 papers · 1 filter
Efficient Application of Tensor Network Operators to Tensor Network States Through Successive Deterministic Compression
Richard M. Milbradt, Shuo Sun, Christian B. Mendl +2
We introduce an algorithm to apply tree tensor network operators to tree tensor network states followed by compression. Especially when applying a matrix product operator to matrix…
Partitioned Expansions for Approximate Tensor Network Contractions
Glen Evenbly, Johnnie Gray, Garnet Kin-Lic Chan
We propose a method for approximating the contraction of a tensor network by partitioning the network into a sum of computationally cheaper networks. This method, which we call a p…
Tensor Network Loop Cluster Expansions for Quantum Many-Body Problems
Johnnie Gray, Gunhee Park, Glen Evenbly +3
We analyze the tensor network loop cluster expansion, introduced in [G. Park, J. Gray, and G. K.-L. Chan, Phys. Rev. B 112, 174310 (2025)] as a systematic correction to belief prop…
Simulating quantum dynamics in two-dimensional lattices with tensor network influence functional belief propagation
Gunhee Park, Johnnie Gray, Garnet Kin-Lic Chan
Describing nonequilibrium quantum dynamics remains a significant computational challenge due to the growth of spatial entanglement. The tensor network influence functional (TN-IF)…
Rapid quantum ground state preparation via dissipative dynamics
Yongtao Zhan, Zhiyan Ding, Jakob Huhn +4
Inspired by natural cooling processes, dissipation has become a promising approach for preparing low-energy states of quantum systems. However, the potential of dissipative protoco…
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…
Loop Series Expansions for Tensor Networks
Glen Evenbly, Nicola Pancotti, Ashley Milsted +2
Belief propagation (BP) can be a useful tool to approximately contract a tensor network, provided that the contributions from any closed loops in the network are sufficiently weak.…
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…