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Quantum Monte Carlo (QMC) methods are one of the most important tools for studying interacting quantum many-body systems. The vast majority of QMC calculations in interacting fermion systems require a constraint to control the sign problem.…

Strongly Correlated Electrons · Physics 2016-12-08 Mingpu Qin , Hao Shi , Shiwei Zhang

The auxiliary-field quantum Monte Carlo (AFQMC) method is a general numerical method for correlated many-electron systems, which is being increasingly applied in lattice models, atoms, molecules, and solids. Here we introduce the theory and…

Computational Physics · Physics 2021-02-24 Hao Shi , Shiwei Zhang

We offer a new proposal for the Monte Carlo treatment of many-fermion systems in continuous space. It is based upon Diffusion Monte Carlo with significant modifications: correlated pairs of random walkers that carry opposite signs;…

Condensed Matter · Physics 2009-10-31 M. H. Kalos , Francesco Pederiva

Cluster Perturbation Theory (CPT) is a technique for computing the spectral function of fermionic models with local interactions. By combining the solution of the model on a finite cluster with perturbation theory on intra-cluster hoppings,…

Strongly Correlated Electrons · Physics 2021-07-26 Edwin W. Huang , Yao Wang

The Diffusion Monte Carlo method with constant number of walkers, also called Stochastic Reconfiguration as well as Sequential Monte Carlo, is a widely used Monte Carlo methodology for computing the ground-state energy and wave function of…

Statistics Theory · Mathematics 2024-12-09 Michel Caffarel , Pierre del Moral , Luc de Montella

We present a finite-temperature canonical-ensemble determinant quantum Monte Carlo algorithm that enforces an exact fermion number and enables stable simulations of correlated lattice fermions. We propose a stabilized QR update that reduces…

Strongly Correlated Electrons · Physics 2026-01-21 Tu Hong , Kun Chen , Xiao Yan Xu

This topical review describes the methodology of continuum variational and diffusion quantum Monte Carlo calculations. These stochastic methods are based on many-body wave functions and are capable of achieving very high accuracy. The…

Materials Science · Physics 2010-02-11 R. J. Needs , M. D. Towler , N. D. Drummond , P. Lopez Rios

We apply diffusion quantum Monte Carlo (DMC) to a broad set of solids, benchmarking the method by comparing bulk structural properties (equilibrium volume and bulk modulus) to experiment and DFT based theories. The test set includes…

Materials Science · Physics 2015-06-17 Luke Shulenburger , Thomas R. Mattsson

We investigate the two-dimensional cooperon-fermion model in the correlated regime with a new continuous-time diagrammatic determinant quantum Monte Carlo (DDQMC) algorithm. We estimate the transition temperature $T_{c}$, examine the…

Superconductivity · Physics 2015-03-19 Kai-Yu Yang , E. Kozik , Xin Wang , M. Troyer

We demonstrate the applicability of a recently proposed multiscale thermalization algorithm to two-color quantum chromodynamics (QCD) with two mass-degenerate fermion flavors. The algorithm involves refining an ensemble of gauge…

High Energy Physics - Lattice · Physics 2016-12-06 William Detmold , Michael G. Endres

Many-electron problems pose some of the greatest challenges in computational science, with important applications across many fields of modern science. Fermionic quantum Monte Carlo (QMC) methods are among the most powerful approaches to…

Monte Carlo sampling is a powerful toolbox of algorithmic techniques widely used for a number of applications wherein some noisy quantity, or summary statistic thereof, is sought to be estimated. In this paper, we survey the literature for…

We introduce version 2.0 of the SmoQyDQMC.jl package, a Julia implementation of the determinant quantum Monte Carlo algorithm. SmoQyDQMC.jl supports generalized tight-binding Hamiltonians with local and extended Hubbard and generalized…

Numerically exact continuous-time Quantum Monte Carlo algorithm for finite fermionic systems with non-local interactions is proposed. The scheme is particularly applicable for general multi-band time-dependent correlations since it does not…

Strongly Correlated Electrons · Physics 2009-11-10 A. N. Rubtsov , A. I. Lichtenstein

An exact, nonlocal, finite step-size algorithm for Monte Carlo simulation of theories with dynamical fermions is proposed. The algorithm is based on obtaining the new configuration U' from the old one U by solving the equation $ M(U') \eta…

High Energy Physics - Lattice · Physics 2009-11-07 T. Bakeyev

We propose a general framework of quantum kinetic Monte Carlo algorithm, based on a stochastic representation of a series expansion of the quantum evolution. Two approaches have been developed in the context of quantum many-body spin…

Computational Physics · Physics 2018-01-17 Zhenning Cai , Jianfeng Lu

The quantum Monte Carlo (QMC) is one of the most promising many-body electronic structure approaches. It employs stochastic techniques for solving the stationary Schr\" odinger equation and for evaluation of expectation values. The key…

Other Condensed Matter · Physics 2007-12-20 Michal Bajdich

Quantum Monte Carlo (QMC) methods have received considerable attention over the last decades due to their great promise for providing a direct solution to the many-body Schrodinger equation in electronic systems. Thanks to their low scaling…

Chemical Physics · Physics 2013-03-28 M. A. Morales , J. McMinis , B. K. Clark , J. Kim , G. Scuseria

Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum many-body systems. One of its bottlenecks is the lack of general and efficient update algorithm for large size systems close to phase transition or with…

Strongly Correlated Electrons · Physics 2017-01-11 Junwei Liu , Yang Qi , Zi Yang Meng , Liang Fu

Quantum Monte Carlo (QMC) is a family of powerful tools for addressing quantum many-body problems. However, its applications are often plagued by the fermionic sign problem. A promising strategy is to simulate an interaction without sign…

Nuclear Theory · Physics 2025-05-01 Jun Liu , Teng Wang , Bing-Nan Lu