Tootfinder

Finite temperature dopant-induced spin reorganization explored via tensor networks in the two-dimensional $t$-$J$ model
Yintati Zhang, Aritra Sinha, Marek M. Rams, Jacek Dziarmaga
https://arxiv.org/abs/2510.04756

Finite temperature dopant-induced spin reorganization explored via tensor networks in the two-dimensional $t$-$J$ model
Doped Mott insulators host intertwined spin-charge phenomena that evolve with temperature and can culminate in stripe order or superconductivity at low temperatures. The two-dimensional $t$-$J$ model captures this interplay yet finite-temperature, infinite-size calculations remain difficult. Using purification represented by a tensor network - an infinite projected entangled-pair state (iPEPS) ansatz - we simulate the $t$-$J$ model at finite temperature directly in the thermodynamic limit, reac…

Congestion bounds via Laplacian eigenvalues and their application to tensor networks with arbitrary geometry
Sayan Mukherjee, Shinichiro Akiyama
https://arxiv.org/abs/2510.02725

Congestion bounds via Laplacian eigenvalues and their application to tensor networks with arbitrary geometry
Embedding the vertices of arbitrary graphs into trees while minimizing some measure of overlap is an important problem with applications in computer science and physics. In this work, we consider the problem of bijectively embedding the vertices of an $n$-vertex graph $G$ into the leaves of an $n$-leaf rooted binary tree $\mathcal{B}$. The congestion of such an embedding is given by the largest size of the cut induced by the two components obtained by deleting any vertex of $\mathcal{B}$. The c…

Noise-reduced stochastic resolution of identity to CC2 for large-scale calculations via tensor hypercontraction
Chongxiao Zhao, Wenjie Dou
https://arxiv.org/abs/2509.21885 https…

Noise-reduced stochastic resolution of identity to CC2 for large-scale calculations via tensor hypercontraction
The stochastic resolution of identity (sRI) approximation significantly reduces the computational scaling of CC2 from O(N^5) to O(N^3), where N is a measure of system size. However, the inherent stochastic noise, while controllable, can introduce substantial errors in energy derivatives, limiting its reliability for molecular dynamics simulations. To mitigate this limitation, we introduce a noise-reduced approach, termed THC-sRI-CC2, which synergistically combines the sRI framework with tensor …