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The density-matrix renormalization group in the age of matrix product states

9 Pith papers cite this work. Polarity classification is still indexing.

9 Pith papers citing it
abstract

The density-matrix renormalization group method (DMRG) has established itself over the last decade as the leading method for the simulation of the statics and dynamics of one-dimensional strongly correlated quantum lattice systems. In the further development of the method, the realization that DMRG operates on a highly interesting class of quantum states, so-called matrix product states (MPS), has allowed a much deeper understanding of the inner structure of the DMRG method, its further potential and its limitations. In this paper, I want to give a detailed exposition of current DMRG thinking in the MPS language in order to make the advisable implementation of the family of DMRG algorithms in exclusively MPS terms transparent. I then move on to discuss some directions of potentially fruitful further algorithmic development: while DMRG is a very mature method by now, I still see potential for further improvements, as exemplified by a number of recently introduced algorithms.

years

2026 9

representative citing papers

Symmetry breaking phases and transitions in an Ising fusion category lattice model

cond-mat.str-el · 2026-04-22 · unverdicted · novelty 7.0

The Ising fusion category lattice model features a symmetric critical phase equivalent to the Ising model, a categorical ferromagnetic phase with threefold degeneracy, and a critical categorical antiferromagnetic phase with fourfold degeneracy described by an Ising CFT.

Preparing High-Fidelity Thermofield Double States

quant-ph · 2026-05-04 · unverdicted · novelty 6.0

A gapped parent Hamiltonian built from two copies of a target Hamiltonian plus ultra-local inter-copy couplings allows adiabatic preparation of high-fidelity thermofield double states for ETH-obeying systems.

Entanglement is Half the Story: Post-Selection vs. Partial Traces

quant-ph · 2026-05-04 · unverdicted · novelty 4.0

A hybrid tensor network framework interpolates between classical and quantum models via controllable post-selection, with a trainable hyperparameter that complements bond dimension to enhance quantum machine learning.

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Showing 9 of 9 citing papers.