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quant-ph2025
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…
quant-ph2024
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…