Finite-Temperature Study of the Hubbard Model via Enhanced Exponential Tensor Renormalization Group
Changkai Zhang, Jan von Delft
TL;DR
This paper tackles the challenging finite-temperature study of the 2D Hubbard model by introducing an enhanced 1s+ eXponential Tensor Renormalization Group (XTRG) algorithm that leverages Controlled Bond Expansion to enlarge the variational space. The method attains near 2-site accuracy at approximately 1-site cost, delivering up to a 50% speedup and enabling cooling to $T/t\approx0.004$, which permits direct comparison with zero-temperature iPEPS results and exploration of superconducting correlations. Key findings include pairing enhancement at larger doping and positive $t'/t$, a pseudogap onset temperature $T^*$ that decreases with doping (especially for $t'/t>0$), and a possible Nagaoka polaron signature; a comprehensive density-matrix snapshot dataset is also generated for AI-driven analyses and cold-atom comparisons. Overall, the approach provides a scalable, symmetry-preserving framework for finite-temperature investigations of the Hubbard model with practical implications for quantum simulations and experimental benchmarking.
Abstract
The two-dimensional (2D) Hubbard model has long attracted interest for its rich phase diagram and its relevance to high-$T_c$ superconductivity. However, reliable finite-temperature studies remain challenging due to the exponential complexity of many-body interactions. Here, we introduce an enhanced $1\text{s}^+$ eXponential Tensor Renormalization Group algorithm that enables efficient finite-temperature simulations of the 2D Hubbard model. By exploring an expanded space, our approach achieves two-site update accuracy at the computational cost of a one-site update, and delivers up to 50% acceleration for Hubbard-like systems, which enables simulations down to $T\!\approx\!0.004t$. This advance permits a direct investigation of superconducting order over a wide temperature range and facilitates a comparison with zero-temperature infinite Projected Entangled Pair State simulations. Finally, we compile a comprehensive dataset of snapshots spanning the relevant region of the phase diagram, providing a valuable reference for Artificial Intelligence-driven analyses of the Hubbard model and a comparison with cold-atom experiments.
