Fermionic tensor network contraction for arbitrary geometries

作者：Yang Gao, Huanchen Zhai, Johnnie Gray, Ruojing Peng, Gunhee Park, Wen-Yuan Liu, Eirik F. Kjønstad, Garnet Kin‐Lic Chan · 发表于：Physical Review Research · 年份：2025 · DOI：10.1103/physrevresearch.7.023193 · 被引用次数：5 · 研究领域：Model Reduction and Neural Networks、Computational Physics and Python Applications、Control and Stability of Dynamical Systems

We describe our implementation of fermionic tensor network contraction on arbitrary lattices within both a globally ordered and a locally ordered formalism. We provide a pedagogical description of these two conventions as implemented for the quimb library. Using hyperoptimized approximate contraction strategies, we present benchmark fermionic projected entangled pair state simulations of finite Hubbard models defined on the three-dimensional diamond lattice and random regular graphs.