16 citations · 46 across the 9 of their papers we have counts for
9 papers
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.…
Hyperoptimized approximate contraction of tensor networks for rugged-energy-landscape spin glasses on periodic square and cubic lattices
Adil A. Gangat, Johnnie Gray
Obtaining the low-energy configurations of spin glasses that have rugged energy landscapes is of direct relevance to combinatorial optimization and fundamental science. Search-base…
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…