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A new Markov Chain Monte Carlo method for simulating the dynamics of molecular systems characterized by hard-core interactions is introduced. In contrast to traditional Kinetic Monte Carlo approaches, where the state of the system is…

Computational Physics · Physics 2017-02-07 Liborio I. Costa

Among many types of quantum entanglement properties, the entanglement spectrum provides more abundant information than other observables. Exact diagonalization and density matrix renormalization group method could handle the system in…

Strongly Correlated Electrons · Physics 2025-03-05 Weilun Jiang , Xiaofan Luo , Bin-Bin Mao , Zheng Yan

This chapter is devoted to the computation of equilibrium (thermodynamic) properties of quantum systems. In particular, we will be interested in the situation where the interaction between particles is so strong that it cannot be treated as…

Mesoscale and Nanoscale Physics · Physics 2016-02-03 Alexei Filinov , Jens Böning , Michael Bonitz

A continuous-time projection quantum Monte Carlo algorithm is employed to simulate the ground state of a short-range quantum spin-glass model, namely, the two-dimensional Edwards-Anderson Hamiltonian with transverse field, featuring…

Disordered Systems and Neural Networks · Physics 2024-12-24 L. Brodoloni , S. Pilati

Elastic systems that are spatially heterogeneous in their mechanical response pose special challenges for molecular simulations. Standard methods for sampling thermal fluctuations of a system's size and shape proceed through a series of…

Materials Science · Physics 2015-05-13 Sander Pronk , Phillip L. Geissler

A model-independent approach capable of extracting spin-wave frequencies and displacement vectors from ensembles of supercell spin configurations is presented. The method is appropriate for those systems whose spin-dynamical motion is well…

Other Condensed Matter · Physics 2007-05-23 Andrew L. Goodwin , Martin T. Dove , Matthew G. Tucker , David A. Keen

Employing a classical density-functional description of liquid environments, we introduce a rigorous method for the diffusion quantum Monte Carlo calculation of free energies and thermodynamic averages of solvated systems that requires…

Ab initio calculations play an essential role in our fundamental understanding of quantum many-body systems across many subfields, from strongly correlated fermions to quantum chemistry and from atomic and molecular systems to nuclear…

The Monte Carlo wave function method or the quantum trajectory/jump approach is a powerful tool to study dissipative dynamics governed by the Markovian master equation, in particular for high-dimensional systems and when it is difficult to…

Quantum Physics · Physics 2009-11-13 X. L. Huang , H. Y. Sun , X. X. Yi

A simple technique is proposed for numerically determining equilibrium ion distribution functions belonging to free energies of the Poisson-Boltzmann type. The central idea is to perform a conventional Monte-Carlo simulation using the free…

Soft Condensed Matter · Physics 2009-10-31 Markus Deserno

We review a recently devised Monte Carlo simulation method for the direct study of quasi-stationary properties of stochastic processes with an absorbing state. The method is used to determine the static correlation function and the…

Statistical Mechanics · Physics 2009-11-10 Ronald Dickman , Marcelo Martins de Oliveira

Particles and fields are standard components in numerical simulations like transport simulations in nuclear physics and have very well understood dynamics. Still, a common problem is the interaction between particles and fields due to their…

High Energy Physics - Phenomenology · Physics 2015-04-16 Christian Wesp , Hendrik van Hees , Alex Meistrenko , Carsten Greiner

We argue that one can associate a pseudo-time with sequences of configurations generated in the course of classical Monte Carlo simulations for a single-minimum bound state, if the sampling is optimal. Hereby the sampling rates can be,…

Statistical Mechanics · Physics 2023-05-29 Yang He , Vassiliy Lubchenko

We investigate the semiclassical dynamics of massless Dirac fermions in 2+1 dimensions in the presence of external electromagnetic fields. By generalizing the $\alpha$ matrices to the spin-$S$ matrices and doing a certain scaling, we…

Mesoscale and Nanoscale Physics · Physics 2015-05-30 Moitri Maiti , R. Shankar

A quantum Monte Carlo algorithm is constructed starting from the standard perturbation expansion in the interaction representation. The resulting configuration space is strongly related to that of the Stochastic Series Expansion (SSE)…

Strongly Correlated Electrons · Physics 2009-10-30 A. W. Sandvik , R. R. P. Singh , D. K. Campbell

Direct sampling of multi-dimensional systems with quantum Monte Carlo methods allows exact account of many-body effects or particle correlations. The most straightforward approach to solve the Schr\"odinger equation, Diffusion Monte Carlo,…

Quantum Physics · Physics 2017-09-07 Ilkka Ruokosenmäki , Tapio T. Rantala

The problem of Monte-Carlo method study at computer simulation lessons in a Teachers' Training Institute is reviewed in the article. The suggested technique envisages the simulation modelling of various stochastic processes. They include…

Computers and Society · Computer Science 2014-01-10 R. V. Mayer

We numerically study the Euclidean quantum cosmology of a closed, homogeneous and isotropic universe with a cosmological constant. A dust field acts as a clock, and we compute the ground state wavefunction, correlation function, and mean…

General Relativity and Quantum Cosmology · Physics 2025-09-30 Malaik Kabir , Syed Moeez Hassan

We derive a "classical-quantum" approximation scheme for a broad class of bipartite quantum systems from fully quantum dynamics. In this approximation, one subsystem evolves via classical equations of motion with quantum corrections, and…

Quantum Physics · Physics 2023-06-05 Viqar Husain , Irfan Javed , Sanjeev S. Seahra , Nomaan X

Monte Carlo methods are widely used importance sampling techniques for studying complex physical systems. Integrating these methods with deep learning has significantly improved efficiency and accuracy in high-dimensional problems and…

Disordered Systems and Neural Networks · Physics 2024-12-24 Yixiong Ren , Jianhui Zhou
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