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In this review paper, we illustrate a possible route to obtain a reliable solution of the 2D Hubbard model and an explanation for some of the unconventional behaviours of underdoped high-$T_\text{c}$ cuprate superconductors within the…

Strongly Correlated Electrons · Physics 2018-09-28 A. Di Ciolo , A. Avella

We study by computer simulation the nucleation of a supersaturated Lennard-Jones vapor into the liquid phase. The large free energy barriers to transition make the time scale of this process impossible to study by ordinary molecular…

Statistical Mechanics · Physics 2018-08-15 Pablo M. Piaggi , Omar Valsson , Michele Parrinello

We introduce a variational hybrid classical-quantum algorithm to simulate the Lindblad master equation and its adjoint for time-evolving Markovian open quantum systems and quantum observables. Our method is based on a direct representation…

Quantum Physics · Physics 2023-05-19 Tasneem Watad , Netanel H. Lindner

We study cluster perturbation theory [Phys. Rev. Lett. \textbf{84}, 522 (2000)] when auxiliary field quantum Monte Carlo method is used for solving the cluster hamiltonian. As a case study, we calculate the spectral functions of the Hubbard…

Strongly Correlated Electrons · Physics 2007-05-23 Fei Lin , Erik S. Sorensen , Catherine Kallin , A. John Berlinsky

The use of combinatorial optimization algorithms has contributed substantially to the major progress that has occurred in recent years in the understanding of the physics of disordered systems, such as the random-field Ising model. While…

Disordered Systems and Neural Networks · Physics 2023-02-22 Manoj Kumar , Martin Weigel

The Hubbard model is one of the primary models for understanding the essential many-body physics in condensed matter systems such as Mott insulators and cuprate high-Tc superconductors. Recent advances in atomically precise fabrication in…

The self-consistent solution for the spin-charge solitonic superstructure in quasi-one-dimensional electron system is obtained in the framework of the Hubbard model as a function of a hole doping. Effects of interchain interactions on the…

Condensed Matter · Physics 2009-10-31 S. I. Matveenko , S. I. Mukhin

The Fermi-Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum…

Quantum Physics · Physics 2026-05-27 Talal Ahmed Chowdhury , Vladimir Korepin , Vincent R. Pascuzzi , Kwangmin Yu

Theory of the superconducting parity transition is extended by incorporating the vortex degree of freedom. We employ the bilayer Rashba model representing locally noncentrosymmetric layered superconductors and derive the Ginzburg-Landau…

Superconductivity · Physics 2025-01-22 Akihiro Minamide , Youichi Yanase

We describe a general strategy, PERM (Pruned-Enriched Rosenbluth Method), for sampling configurations from a given Gibbs-Boltzmann distribution. The method is not based on the Metropolis concept of establishing a Markov process whose…

Soft Condensed Matter · Physics 2007-05-23 P. Grassberger , und H. Frauenkron

A method of cluster diagonalization in a systematically expanded Hilbert space is described. We discuss some applications of this procedure to models of high-T_c superconductors, like the t - J and one and three bands Hubbard models in two…

Condensed Matter · Physics 2009-10-22 Jose' Riera , Elbio Dagotto

Cluster algorithms are developed for simulating quantum spin systems like the one- and two-dimensional Heisenberg ferro- and anti-ferromagnets. The corresponding two- and three-dimensional classical spin models with four-spin couplings are…

High Energy Physics - Lattice · Physics 2019-06-05 U. -J. Wiese , H. -P. Ying

Cluster algorithms are developed for simulating quantum spin systems like the one- and two-dimensional Heisenberg ferro- and anti-ferromagnets. The corresponding two- and three-dimensional classical spin models with four-spin couplings are…

Condensed Matter · Physics 2009-10-22 U. -J. Wiese , H. -P. Ying

We show that the physics of the SU($N$) Hubbard model can be realistically simulated with the recently developed orbital Hatsugai-Kohmoto model. In this approach, the momentum mixing absent from the band Hatsugai-Kohmoto model is included…

Strongly Correlated Electrons · Physics 2025-06-27 Nico A. Hackner , Peizhi Mai , Philip W. Phillips

A wide class of coupled-cluster methods is introduced, based on Arponen's extended coupled-cluster theory. This class of methods is formulated in terms of a coordinate transformation of the cluster operators. The mathematical framework for…

Chemical Physics · Physics 2020-12-02 Simen Kvaal , Andre Laestadius , Tilmann Bodenstein

Significant advances in numerical techniques have enabled recent breakthroughs in the study of various properties of the Hubbard model - a seemingly simple, yet complex model of correlated electrons that has been a focus of study for more…

Strongly Correlated Electrons · Physics 2021-09-14 E. W. Huang , W. O. Wang , J. K. Ding , T. Liu , F. Liu , X. -X. Huang , B. Moritz , T. P. Devereaux

In the framework of the hybrid quantum-classical variational cluster approach (VCA) to strongly correlated electron systems one of the goals of a quantum subroutine is to find single-particle correlation functions of lattice fermions in…

Quantum Physics · Physics 2025-06-26 Gino Bishop , Dmitry Bagrets , Frank K. Wilhelm

A quantum Monte Carlo method with non-local update scheme is presented. The method is based on a path-integral decomposition and a worm operator which is local in imaginary time. It generates states with a fixed number of particles and…

Statistical Mechanics · Physics 2009-11-11 Kris Van Houcke , Stefan Rombouts , Lode Pollet

Building on the established methods for superconducting circuit quantization, we present a new theoretical framework for approximate numerical simulation of Josephson quantum circuits. Simulations based on this framework provide access to a…

Quantum Physics · Physics 2020-12-17 Andrew J. Kerman

The hidden Markov model (HMM) is a generative model that treats sequential data under the assumption that each observation is conditioned on the state of a discrete hidden variable that evolves in time as a Markov chain. In this paper, we…

Artificial Intelligence · Computer Science 2011-09-07 Emanuele Coviello , Antoni B. Chan , Gert R. G. Lanckriet