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Tensor ring (TR) decomposition has recently received increased attention due to its superior expressive performance for high-order tensors. However, the applicability of traditional TR decomposition algorithms to real-world applications is…

Machine Learning · Computer Science 2023-05-17 Yicong He , George K. Atia

We present the theory of a density matrix renormalization group (DMRG) algorithm which can solve for both the ground and excited states of non-Hermitian transcorrelated Hamiltonians, and show applications in \emph{ab initio} molecular…

Tensor network (TN) methods are well established for computing partition functions in statistical mechanics, though this use has traditionally been limited to lattice models. We extend the scope of TN methodology to interacting particle…

Statistical Mechanics · Physics 2026-04-29 Gunhee Park , Tomislav Begušić , Si-Jing Du , Johnnie Gray , Garnet Kin-Lic Chan

We systematically study a numerical procedure that reveals the asymptotically self-similar dynamics of solutions of partial differential equations (PDEs). This procedure, based on the renormalization group (RG) theory for PDEs, appeared…

Numerical Analysis · Mathematics 2018-07-12 Gastão A. Braga , Federico C. Furtado , Vincenzo Isaia , Long Lee

We propose a new fast numerical renormalization group method,the corner transfer matrix renormalization group (CTMRG) method, which is based on a unified scheme of Baxter's corner transfer matrix method and White's density matrix…

Condensed Matter · Physics 2009-10-28 T. Nishino , K. Okunishi

A renormalization group flow of Hamiltonians for two-dimensional classical partition functions is constructed using tensor networks. Similar to tensor network renormalization ([G. Evenbly and G. Vidal, Phys. Rev. Lett. 115, 180405 (2015)],…

Statistical Mechanics · Physics 2017-06-29 Matthias Bal , Michaël Mariën , Jutho Haegeman , Frank Verstraete

Tensor network decompositions offer an efficient description of certain many-body states of a lattice system and are the basis of a wealth of numerical simulation algorithms. We discuss how to incorporate a global symmetry, given by a…

Strongly Correlated Electrons · Physics 2010-11-19 Sukhwinder Singh , Robert N. C. Pfeifer , Guifre Vidal

Tensor ring (TR) decomposition is a simple but effective tensor network for analyzing and interpreting latent patterns of tensors. In this work, we propose a doubly randomized optimization framework for computing TR decomposition. It can be…

Numerical Analysis · Mathematics 2023-03-30 Yajie Yu , Hanyu Li , Jingchun Zhou

We report on an exhaustive investigation of the dynamical dimer-dimer correlations in imaginary time for the quantum dimer model on the triangular lattice using the Green's function Monte Carlo method. We show in particular that soft modes…

Strongly Correlated Electrons · Physics 2007-05-23 Arnaud Ralko , Michel Ferrero , Federico Becca , Dmitri Ivanov , Frederic Mila

We propose a new tensor renormalization group algorithm, Anisotropic Tensor Renormalization Group (ATRG), for lattice models in arbitrary dimensions. The proposed method shares the same versatility with the Higher-Order Tensor…

Statistical Mechanics · Physics 2020-09-02 Daiki Adachi , Tsuyoshi Okubo , Synge Todo

We show that finding the classical bound of broad families of Bell inequalities can be naturally framed as the contraction of an associated tensor network, but in tropical algebra, where the sum is replaced by the minimum and the product is…

Quantum Physics · Physics 2026-02-13 Mengyao Hu , Jordi Tura

We show a way to perform the canonical renormalization group (RG) prescription in tensor space: write down the tensor RG equation, linearize it around a fixed-point tensor, and diagonalize the resulting linearized RG equation to obtain…

Statistical Mechanics · Physics 2021-04-20 Xinliang Lyu , RuQing G. Xu , Naoki Kawashima

The nonequilibrium Green's function (NEGF) formalism is a powerful tool to study the nonequilibrium dynamics of correlated lattice systems, but its applicability to realistic system sizes and long timescales is limited by unfavorable memory…

Strongly Correlated Electrons · Physics 2025-09-29 Maksymilian Środa , Ken Inayoshi , Michael Schüler , Hiroshi Shinaoka , Philipp Werner

We study the dynamical density matrix renormalization group (DDMRG) and time-dependent density matrix renormalization group (td-DMRG) algorithms in the ab initio context, to compute dynamical correlation functions of correlated systems. We…

Chemical Physics · Physics 2017-11-21 Enrico Ronca , Zhendong Li , Carlos A. Jimenez-Hoyos , Garnet Kin-Lic Chan

We present an efficient implementation of the Density Matrix Renormalization Group (DMRG) algorithm that includes an optimal ordering of the proton and neutron orbitals and an efficient expansion of the active space utilizing various…

Nuclear Theory · Physics 2015-11-18 Ö. Legeza , L. Veis , A. Poves , J. Dukelsky

The physical properties of a quantum many-body system can, in principle, be determined by diagonalizing the respective Hamiltonian, but the dimensions of its matrix representation scale exponentially with the number of degrees of freedom.…

Strongly Correlated Electrons · Physics 2023-09-13 G. Catarina , Bruno Murta

This paper tackles the problem of recovering a low-rank signal tensor with possibly correlated components from a random noisy tensor, or so-called spiked tensor model. When the underlying components are orthogonal, they can be recovered…

Machine Learning · Statistics 2023-03-20 Mohamed El Amine Seddik , Mohammed Mahfoud , Merouane Debbah

Understanding the equilibrium properties and out of equilibrium dynamics of quantum field theories are key aspects of fundamental problems in theoretical particle physics and cosmology. However, their classical simulation is highly…

Quantum Physics · Physics 2023-12-21 Philipp Schmoll , Jan Naumann , Alexander Nietner , Jens Eisert , Spyros Sotiriadis

We present a new strategy for contracting tensor networks in arbitrary geometries. This method is designed to follow as strictly as possible the renormalization group philosophy, by first contracting tensors in an exact way and, then,…

Strongly Correlated Electrons · Physics 2013-05-23 A. Garcia-Saez , J. I. Latorre

We examine feasibility of accurate estimations of universal critical data using tensor renormalization group (TRG) algorithm introduced by Levin and Nave. Specifically, we compute critical exponents $\gamma, \gamma/\nu, \delta, \eta$ and…

Statistical Mechanics · Physics 2022-04-15 Sankhya Basu , Vadim Oganesyan