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In this paper we apply the time-dependent quantum Monte Carlo (TDQMC) method to explore a midified single- and double-slit diffraction of matter waves. By using a simplified model of two electrons prepared in the ground state of an atom…

Quantum Physics · Physics 2025-02-07 Ivan P. Christov

We develop exact Markov chain Monte Carlo methods for discretely-sampled, directly and indirectly observed diffusions. The qualification "exact" refers to the fact that the invariant and limiting distribution of the Markov chains is the…

The diffusion quantum Monte Carlo method is extended to solve the old theoretical physics problem of many-electron atoms and ions in intense magnetic fields. The feature of our approach is the use of adiabatic approximation wave functions…

Quantum Physics · Physics 2009-11-13 S. Bucheler , D. Engel , J. Main , G. Wunner

The search for two-dimensional (2D) magnetic materials has attracted a great deal of attention because of the experimental synthesis of 2D CrI$_3$, which has a measured Curie temperature of 45 K. Often times, these monolayers have a higher…

Strongly Correlated Electrons · Physics 2023-01-16 Daniel Wines , Kamal Choudhary , Francesca Tavazza

We show that recently developed quantum Monte Carlo methods, which provide accurate vertical transition energies for single excitations, also successfully treat double excitations. We study the double excitations in medium-sized molecules,…

We present Diffusion Restore, a real-time framework for diffusion-based MCMC light transport. MCMC methods are highly suitable for sampling from complex high-dimensional distributions and for approximating integrals over them. In practice,…

Computational Engineering, Finance, and Science · Computer Science 2026-05-21 Sascha Holl , Gurprit Singh , Hans-Peter Seidel

This paper considers the problem of sampling from non-logconcave distribution, based on queries of its unnormalized density. It first describes a framework, Denoising Diffusion Monte Carlo (DDMC), based on the simulation of a denoising…

Machine Learning · Statistics 2024-10-31 Ye He , Kevin Rojas , Molei Tao

Hamiltonian Monte Carlo (HMC) has emerged as a powerful Markov Chain Monte Carlo (MCMC) method to sample from complex continuous distributions. However, a fundamental limitation of HMC is that it can not be applied to distributions with…

Computation · Statistics 2021-12-10 Guangyao Zhou

In this paper, we combine thermal effects with Landau-Zener (LZ) quantum tunneling effects in a dynamical Monte Carlo (DMC) framework to produce satisfactory magnetization curves of single-molecule magnet (SMM) systems. We use the giant…

Mesoscale and Nanoscale Physics · Physics 2010-10-14 Gui-Bin Liu , Bang-Gui Liu

The recently developed density matrix quantum Monte Carlo (DMQMC) algorithm stochastically samples the N -body thermal density matrix and hence provides access to exact properties of many-particle quantum systems at arbitrary temperatures.…

Strongly Correlated Electrons · Physics 2015-10-15 Fionn D. Malone , N. S. Blunt , James J. Shepherd , D. K. K. Lee , J. S. Spencer , W. M. C. Foulkes

One of the most significant drawbacks of the all-electron ab initio diffusion Monte Carlo (DMC) is that its computational cost drastically increases with the atomic number ($Z$), which typically scales with $Z^{\sim 6}$. In this study, we…

Computational Physics · Physics 2020-04-15 Kousuke Nakano , Ryo Maezono , Sandro Sorella

Implicit Monte Carlo (IMC) and Discrete Diffusion Monte Carlo (DDMC) are methods used to stochastically solve the radiative transport and diffusion equations, respectively. These methods combine into a hybrid transport-diffusion method we…

High Energy Astrophysical Phenomena · Physics 2015-06-22 Ryan T. Wollaeger , Daniel R. van Rossum

We present an exact quantum Monte Carlo method for spin systems coupled to dissipative bosonic baths which makes use of nonlocal wormhole updates to simulate the retarded spin-flip interactions originating from an off-diagonal spin-boson…

Strongly Correlated Electrons · Physics 2022-05-17 Manuel Weber

Metastable condensed matter typically fluctuates about local energy minima at the femtosecond time scale before transitioning between local minima after nanoseconds or microseconds. This vast scale separation limits the applicability of…

Computational Physics · Physics 2017-11-22 Brittan A Farmer , Mitchell Luskin , Petr Plecháč , Gideon Simpson

We present a version of the T-moves approach for treating nonlocal pseudopotentials in diffusion Monte Carlo which has much smaller time-step errors than the existing T-moves approaches, while at the same time preserving desirable features…

Chemical Physics · Physics 2021-07-29 Tyler A. Anderson , C. J. Umrigar

We present a novel Exchange Monte Carlo (EMC) method designed for application in continuous-space Path Integral Monte Carlo (PIMC) simulations at finite temperature. Traditional PIMC methods for bosonic systems suffer from long…

Statistical Mechanics · Physics 2026-05-26 Xun Zhao , Synge Todo

A paramount goal in the field of nuclear physics is to unify ab-initio treatments of bound and unbound states. The position-space quantum Monte Carlo (QMC) methods have a long history of successful bound state calculations in light systems…

Nuclear Theory · Physics 2023-08-29 Abraham R. Flores , Kenneth M. Nollett

In this paper we present two efficient implementations of the diffusion approximation to be employed in Monte Carlo computations of radiative transfer in dusty media of massive circumstellar disks. The aim is to improve the accuracy of the…

Instrumentation and Methods for Astrophysics · Physics 2009-11-13 M. Min , C. P. Dullemond , C. Dominik , A. de Koter , J. W. Hovenier

We make a systematical diffusion Monte Carlo (DMC) calculation for all ground state baryons in two confinement scenarios, the pairwise confinement and the three-body flux-tube confinement. With the baryons as an example, we illustrate a…

High Energy Physics - Phenomenology · Physics 2023-03-29 Yao Ma , Lu Meng , Yan-Ke Chen , Shi-Lin Zhu

We created an efficient algorithm suitable for graphics processing units (GPUs) to perform Monte Carlo simulations of a subset of reaction-diffusion models. The algorithm uses techniques that are specific to GPU programming, and combines…

Computational Physics · Physics 2013-03-06 R. D. Schram
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