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The density matrix renormalization group (DMRG) algorithm was originally designed to efficiently compute the zero temperature or ground-state properties of one dimensional strongly correlated quantum systems. The development of the…

Strongly Correlated Electrons · Physics 2016-03-09 A. Nocera , G. Alvarez

We introduce a variational Monte Carlo algorithm for approximating finite-temperature quantum many-body systems, based on the minimization of a modified free energy. This approach directly approximates the state at a fixed temperature,…

Quantum Physics · Physics 2025-02-18 Sirui Lu , Giacomo Giudice , J. Ignacio Cirac

We evaluate, by means of variational calculations, the bound state energy E_B of a pair of charges located on the surface of a cylinder, interacting via Coulomb potential - e^2 / r . The trial wave function involves three variational…

Condensed Matter · Physics 2009-11-07 M. K. Kostov , M. W. Cole , G. D. Mahan

The performance of the density matrix renormalization group (DMRG) is strongly influenced by the choice of the local basis of the underlying physical lattice. We demonstrate that, for the two-dimensional Hubbard model, the hybrid…

Strongly Correlated Electrons · Physics 2017-03-22 G. Ehlers , S. R. White , R. M. Noack

The Corner Transfer Matrix Renormalization Group (CTMRG) algorithm is modified to measure the magnetization at the boundary of the system, including the corners of the square-shaped lattice. Using automatic differentiation, we calculate the…

Statistical Mechanics · Physics 2025-06-23 Roman Krcmar , Jozef Genzor , Andrej Gendiar , Tomotoshi Nishino

For a large class of variational quantum circuits, we show how arbitrary-order derivatives can be analytically evaluated in terms of simple parameter-shift rules, i.e., by running the same circuit with different shifts of the parameters. As…

Quantum Physics · Physics 2021-03-03 Andrea Mari , Thomas R. Bromley , Nathan Killoran

A powerful method for calculating the eigenvalues of a Hamiltonian operator consists of converting the energy eigenvalue equation into a matrix equation by means of an appropriate basis set of functions. The convergence of the method can be…

Quantum Physics · Physics 2007-05-23 Paolo Amore , Alfredo Aranda , Francisco Fernandez , Hugh Jones

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 study the tensor renormalization group (TRG) in the dimension larger than two as the Higher-order TRG (HOTRG) with the randomized SVD method. The randomized SVD and the detailed discussion on the low order tensor representation, we can…

High Energy Physics - Lattice · Physics 2024-01-15 Katsumasa Nakayama

An appropriate iterative scheme for the minimization of the energy, based on the variational Monte Carlo (VMC) technique, is introduced and compared with existing stochastic schemes. We test the various methods for the 1D Heisenberg ring…

Strongly Correlated Electrons · Physics 2009-11-11 Sandro Sorella

We introduce a general corner transfer matrix renormalization group algorithm tailored to projected entangled-pair states on the triangular lattice. By integrating automatic differentiation, our approach enables direct variational energy…

Strongly Correlated Electrons · Physics 2026-01-15 Jan Naumann , Jens Eisert , Philipp Schmoll

A variational ansatz for momentum eigenstates of translation invariant quantum spin chains is formulated. The matrix product state ansatz works directly in the thermodynamic limit and allows for an efficient implementation (cubic scaling in…

We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of…

Machine Learning · Computer Science 2016-05-23 Matthew Hirn , Nicolas Poilvert , Stéphane Mallat

This article compares the tensor method density matrix renormalization group (DMRG) with two neural network based methods -namely FermiNet and PauliNet) for determining the ground state wavefunction of the many-body electronic…

Optimization and Control · Mathematics 2024-12-05 Mathias Dus , Geneviève Dusson , Virginie Ehrlacher , Clément Guillot , Joel Pascal Soffo Wambo

Quantum chemistry calculations such as the prediction of molecular properties and modeling of chemical reactions are a few of the critical areas where near-term quantum computers can showcase quantum advantage. We present a method to…

Quantum Physics · Physics 2022-04-06 Utkarsh Azad , Harjinder Singh

We present a quantum algorithm for simulating complex many-body systems and finding their ground states, combining the use of tensor networks and density matrix renormalization group (DMRG) techniques. The algorithm is based on von…

Quantum Physics · Physics 2026-03-06 Younes Javanmard

We introduce a class of variational states to describe quantum many-body systems. This class generalizes matrix product states which underly the density-matrix renormalization group approach by combining them with weighted graph states.…

Quantum Physics · Physics 2009-11-13 R. Hübener , C. Kruszynska , L. Hartmann , W. Dür , F. Verstraete , J. Eisert , M. B. Plenio

A variant of White's density matrix renormalisation group scheme which is designed to compute low-lying energies of one-dimensional quantum lattice models with a large number of degrees of freedom per site is described. The method is tested…

Condensed Matter · Physics 2009-10-31 R. J. Bursill

We investigate entanglement and quantum phase transition (QPT) in a two-dimensional Heisenberg anisotropic spin-1/2 XY model, using quantum renormalization group method (QRG) on a square lattice of $N\times N$ sites. The entanglement…

Quantum Physics · Physics 2017-08-16 M. Usman , Asif Ilyas , Khalid Khan

We perform quantum simulation on classical and quantum computers and set up a machine learning framework in which we can map out phase diagrams of known and unknown quantum many-body systems in an unsupervised fashion. The classical…

Quantum Physics · Physics 2022-10-21 Korbinian Kottmann