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Related papers: Tensor RG approach to high-temperature fixed point

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The renormalization group (RG) approach is largely responsible for the considerable success that has been achieved in developing a quantitative theory of phase transitions. Physical properties emerge from spectral properties of the…

Mathematical Physics · Physics 2015-05-14 Mei Yin

We introduce an efficient algorithm for reducing bond dimensions in an arbitrary tensor network without changing its geometry. The method is based on a novel, quantitative understanding of local correlations in a network. Together with a…

Strongly Correlated Electrons · Physics 2018-08-23 Markus Hauru , Clement Delcamp , Sebastian Mizera

In recent work we have shown that the Fermi liquid aspects of the strong coupling fixed point of the s-d and Anderson models can brought out more clearly by interpreting the fixed point as a renormalized Anderson model, characterized by a…

Strongly Correlated Electrons · Physics 2009-11-10 A. C. Hewson

Following the construction in arXiv:2210.12127, we develop a symmetry-preserving renormalization group (RG) flow for 3D symmetric theories. These theories are expressed as boundary conditions of a symTFT, which in our case is a 3+1D…

Strongly Correlated Electrons · Physics 2024-12-12 Kaixin Ji , Lin Chen , Li-Ping Yang , Ling-Yan Hung

The full-density-matrix numerical renormalization group (NRG) has evolved as a systematic and transparent setting for the cal- culation of thermodynamical quantities at arbitrary temperatures within the NRG framework. It directly evaluates…

Strongly Correlated Electrons · Physics 2013-05-30 Andreas Weichselbaum

Tensor renormalization group (TRG) constitutes an important methodology for accurate simulations of strongly correlated lattice models. Facilitated by the automatic differentiation technique widely used in deep learning, we propose a…

Strongly Correlated Electrons · Physics 2020-07-07 Bin-Bin Chen , Yuan Gao , Yi-Bin Guo , Yuzhi Liu , Hui-Hai Zhao , Hai-Jun Liao , Lei Wang , Tao Xiang , Wei Li , Z. Y. Xie

We study the classical two-dimensional $\mathrm{RP^2}$ and Heisenberg models, using the Tensor-Network Renormalization (TNR) method. The determination of the phase diagram of these models has been challenging and controversial, owing to the…

Statistical Mechanics · Physics 2024-01-05 Atsushi Ueda , Masaki Oshikawa

Accurate simulations of the two-dimensional (2D) Hubbard model constitute one of the most challenging problems in condensed matter and quantum physics. Here we develop a tangent space tensor renormalization group (tanTRG) approach for the…

Strongly Correlated Electrons · Physics 2023-06-07 Qiaoyi Li , Yuan Gao , Yuan-Yao He , Yang Qi , Bin-Bin Chen , Wei Li

We study the $O(2)$ model with $\mathbb{Z}_4$-symmetric perturbations within the framework of nonperturbative renormalization group (RG) for spatial dimensionality $d=2$ and $d=3$. In a unified framework we resolve the relatively complex…

Statistical Mechanics · Physics 2019-11-13 Andrzej Chlebicki , Pawel Jakubczyk

Extending the parameter space of the three-dimensional (d=3) Ising model, we search for a regime of eliminated corrections to finite-size scaling. For that purpose, we consider a real-space renormalization group (RSRG) with respect to a…

Statistical Mechanics · Physics 2009-11-11 Yoshihiro Nishiyama

We propose a second renormalization group (SRG) in the triad representation of tensor networks. The SRG method improves two parts of the triad tensor renormalization group, which are the decomposition of intermediate tensors and the…

Strongly Correlated Electrons · Physics 2022-05-11 Daisuke Kadoh , Hideaki Oba , Shinji Takeda

Tensor-network renormalization group (TNRG) is an efficient real-space renormalization group method for studying the criticality in both classical and quantum lattice systems. Exploiting symmetries of a system in a TNRG algorithm can…

Statistical Mechanics · Physics 2026-04-17 Xinliang Lyu , Naoki Kawashima

We analyze the renormalization-group (RG) flows of two effective Lagrangians, one for measurement induced transitions of monitored quantum systems and one for entanglement transitions in random tensor networks. These Lagrangians, previously…

Statistical Mechanics · Physics 2024-09-20 Adam Nahum , Kay Joerg Wiese

The tensor-network renormalization group (TNRG) is an accurate numerical real-space renormalization group method for studying phase transitions in both quantum and classical systems. Continuous phase transitions, as an important class of…

Statistical Mechanics · Physics 2026-03-27 Xinliang Lyu

Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional data representation. Despite recent advances, existing TN-SS…

Computer Vision and Pattern Recognition · Computer Science 2026-01-01 Maolin Wang , Bowen Yu , Sheng Zhang , Linjie Mi , Wanyu Wang , Yiqi Wang , Pengyue Jia , Xuetao Wei , Zenglin Xu , Ruocheng Guo , Xiangyu Zhao

Randomly connected tensor networks (RCTN) are the dynamical systems defined by summing over all the possible networks of tensors. Because of the absence of fixed lattice structure, RCTN is not expected to have renormalization procedures. In…

High Energy Physics - Theory · Physics 2025-04-11 Naoki Sasakura

The practical success of polynomial-time tensor network methods for computing ground states of certain quantum local Hamiltonians has recently been given a sound theoretical basis by Arad, Landau, Vazirani, and Vidick. The convergence…

Statistical Mechanics · Physics 2018-02-14 Brenden Roberts , Thomas Vidick , Olexei I. Motrunich

We study perturbative Wilsonian renormalisation group (RG) for the scalar $\phi^4$ theory at finite temperature to one loop order in the Schwinger-Keldysh closed-time-path (CTP) formalism. By explicitly integrating out the UV modes, we show…

High Energy Physics - Theory · Physics 2025-08-26 Giorgio Frangi , Sašo Grozdanov

The renormalization group (RG) is a powerful theoretical framework developed to consistently transform the description of configurations of systems with many degrees of freedom, along with the associated model parameters and coupling…

Statistical Mechanics · Physics 2026-04-20 Andrea Gabrielli , Diego Garlaschelli , Subodh P. Patil , M. Ángeles Serrano

Numerical simulations of strongly correlated fermions at finite temperature are essential for studying high-temperature superconductivity and other quantum many-body phenomena. The recently developed tangent-space tensor renormalization…

Strongly Correlated Electrons · Physics 2026-03-03 Qiaoyi Li , Dai-Wei Qu , Bin-Bin Chen , Tao Shi , Wei Li