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The term Tensor Network States (TNS) refers to a number of families of states that represent different ans\"atze for the efficient description of the state of a quantum many-body system. Matrix Product States (MPS) are one particular case…

High Energy Physics - Lattice · Physics 2014-02-04 Mari Carmen Bañuls , Krzysztof Cichy , J. Ignacio Cirac , Karl Jansen , Hana Saito

Nanoscale topological spin textures in magnetic systems are emerging as promising candidates for scalable quantum architectures. Despite their potential as qubits, previous studies have been limited to semiclassical approaches, leaving a…

Mesoscale and Nanoscale Physics · Physics 2025-08-19 Guanxiong Qu , Ji Zou , Daniel Loss , Tomoki Hirosawa

Over the last decade tensor network states (TNS) have emerged as a powerful tool for the study of quantum many body systems. The matrix product states (MPS) are one particular case of TNS and are used for the simulation of 1+1 dimensional…

High Energy Physics - Lattice · Physics 2015-11-16 Boye Buyens , Karel Van Acoleyen , Jutho Haegeman , Frank Verstraete

For the simulation of equilibrium states and finite-temperature response functions of strongly-correlated quantum many-body systems, we compare the efficiencies of two different approaches in the framework of the density matrix…

Strongly Correlated Electrons · Physics 2017-01-03 Moritz Binder , Thomas Barthel

Quantum turbulence spans length scales from the system size $L$ to the healing length $\xi$, making direct numerical simulations (DNS) of the Gross-Pitaevskii (GP) equation computationally expensive when $L \gg \xi$. We present a matrix…

Matrix product states (MPS) are a central language for one-dimensional quantum matter and a practical target for near-term quantum simulators and variational algorithms. Yet, while substantial effort has focused on preparing MPS with…

Quantum Physics · Physics 2026-04-21 Hyunho Cha , Subin Kim , Jungwoo Lee

The density-matrix renormalization-group (DMRG) algorithm is extended to treat time-dependent problems. The method provides a systematic and robust tool to explore out-of-equilibrium phenomena in quantum many-body systems. We illustrate the…

Mesoscale and Nanoscale Physics · Physics 2009-11-07 M. A. Cazalilla , J. B. Marston

This work presents a comparative study of new and existing optimization and diagonalization methods for solving time-independent partial differential equations (PDEs) using matrix product states (MPS) in the quantized tensor-train formalism…

Quantum Physics · Physics 2026-02-17 Paula García-Molina , Luca Tagliacozzo , Juan José García-Ripoll

We adapt White's density matrix renormalisation group (DMRG) to the direct study of critical phenomena. We use the DMRG to generate transformations in the space of coupling constants. We postulate that a study of density matrix eigenvalues…

Condensed Matter · Physics 2007-05-23 R. J. Bursill , F. Gode

We present a novel technique for the calculation of dynamical correlation functions of quantum impurity systems in equilibrium with Wilson's numerical renormalization group. Our formulation is based on a complete basis set of the Wilson…

Strongly Correlated Electrons · Physics 2009-11-11 Robert Peters , Thomas Pruschke , Frithjof B. Anders

The density matrix renormalization group (DMRG) has been extended to study quantum phase transitions on random graphs of fixed connectivity. As a relevant example, we have analysed the random Ising model in a transverse field. If the…

Disordered Systems and Neural Networks · Physics 2009-11-11 Javier Rodriguez-Laguna

Density matrix renormalization group (DMRG) or matrix product states (MPS) is the most effective and accurate method for studying one-dimensional quantum many-body systems. However, the application of DMRG to two-dimensional systems is not…

Strongly Correlated Electrons · Physics 2024-11-25 Xiangjian Qian , Mingpu Qin

Tensor network algorithms have been remarkably successful solving a variety of problems in quantum many-body physics. However, algorithms to optimize two-dimensional tensor networks known as PEPS lack many of the aspects that make the…

Strongly Correlated Electrons · Physics 2020-04-22 Katharine Hyatt , E. M. Stoudenmire

This article reviews recent developments in the theoretical understanding and the numerical implementation of variational renormalization group methods using matrix product states and projected entangled pair states.

Quantum Physics · Physics 2009-07-17 F. Verstraete , J. I. Cirac , V. Murg

Wilson's numerical renormalization group (NRG) method for the calculation of dynamic properties of impurity models is generalized to investigate the effective impurity model of the dynamical mean field theory at finite temperatures. We…

Strongly Correlated Electrons · Physics 2009-10-31 R. Bulla , T. A. Costi , D. Vollhardt

Numerical renormalization group (NRG) calculations of quantum impurity models, based on a logarithmic discretization in energy of electronic or bosonic Hamiltonians, provide a powerful tool to describe physics involving widely separated…

Strongly Correlated Electrons · Physics 2009-11-13 Axel Freyn , Serge Florens

We introduce a picture to analyze the density matrix renormalization group (DMRG) numerical method from a quantum information perspective. This leads us to introduce some modifications for problems with periodic boundary conditions in which…

Strongly Correlated Electrons · Physics 2007-05-23 F. Verstraete , D. Porras , J. I. Cirac

We use the Matsubara functional renormalization group (FRG) to describe electronic correlations within the single impurity Anderson model. In contrast to standard FRG calculations, we account for the frequency-dependence of the two-particle…

Strongly Correlated Electrons · Physics 2009-11-13 C. Karrasch , R. Hedden , R. Peters , Th. Pruschke , K. Schönhammer , V. Meden

We study the set of random matrix product states (RMPS) introduced in arXiv:0908.3877 as a tool to explore foundational aspects of quantum statistical mechanics. In the present work, we provide an accurate numerical and analytical…

Quantum Physics · Physics 2015-03-13 Silvano Garnerone , Thiago R. de Oliveira , Stephan Haas , Paolo Zanardi

Tensor networks, which are originally developed for characterizing complex quantum many-body systems, have recently emerged as a powerful framework for capturing high-dimensional probability distributions with strong physical…

Machine Learning · Computer Science 2026-03-13 Haotong Duan , Zhongming Chen , Ngai Wong
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