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Markov chain Monte Carlo is widely used in a variety of scientific applications to generate approximate samples from intractable distributions. A thorough understanding of the convergence and mixing properties of these Markov chains can be…

统计方法学 · 统计学 2023-05-23 Saptarshi Chakraborty , Kshitij Khare

The interaction and exchange-correlation contributions to the ground-state energy of an arbitrary many-electron system can be obtained from a spherical average of the wavevector-dependent diagonal structure factor (SF). We model the…

材料科学 · 物理学 2015-06-25 R. Gaudoin , J. M. Pitarke

A simple method for numerical analytic continuation is developed. It is designed to analytically continue the imaginary time (Matsubara frequency) quantum Monte Carlo simulation results to the real time (real frequency) domain. Such a…

计算物理 · 物理学 2018-12-07 Jian Wang , Sudip Chakravarty

High-quality random samples of quantum states are needed for a variety of tasks in quantum information and quantum computation. Searching the high-dimensional quantum state space for a global maximum of an objective function with many local…

量子物理 · 物理学 2015-04-28 Yi-Lin Seah , Jiangwei Shang , Hui Khoon Ng , David John Nott , Berthold-Georg Englert

The efficient probing of spectral features is important for characterising and understanding the structure and dynamics of quantum materials. In this work, we establish a framework for probing the excitation spectrum of quantum many-body…

量子物理 · 物理学 2025-02-11 Jinzhao Sun , Lucia Vilchez-Estevez , Vlatko Vedral , Andrew T. Boothroyd , M. S. Kim

We present an elementary and self-contained account of the analogies existing between classical diffusion and the imaginary-time evolution of quantum systems. These analogies are used to develop a new quantum simulation method which allows…

凝聚态物理 · 物理学 2007-05-23 S. Baroni , S. Moroni

The Quantum Monte Carlo (QMC) method can yield the imaginary-time dependence of a correlation function $C(\tau)$ of an operator $\hat O$. The analytic continuation to real-time proceeds by means of a "numerical inversion" of these data to…

强关联电子 · 物理学 2015-03-11 Nikolaos G. Diamantis , Efstratios Manousakis

In a typical finite temperature quantum Monte Carlo (QMC) simulation, estimators for simple static observables such as specific heat and magnetization are known. With a great deal of system-specific manual labor, one can sometimes also…

统计力学 · 物理学 2026-01-30 Nic Ezzell , Itay Hen

We present a method for fast optimal estimation of the temperature angular power spectrum from observations of the cosmic microwave background. We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from the posterior…

天体物理学 · 物理学 2009-11-13 J. F. Taylor , M. A. J. Ashdown , M. P. Hobson

Inspired by previous works on epidemic-like processes in open quantum systems, we derive an elementary quantum epidemic model that is simple enough to be studied via Quantum Jump Monte Carlo simulations at reasonably large system sizes. We…

统计力学 · 物理学 2025-12-30 Alexander Sturges , Hugo Smith , Matteo Marcuzzi

For a wide class of applications of the Monte Carlo method, we describe a general sampling methodology that is guaranteed to converge to a specified equilibrium distribution function. The method is distinct from that of Metropolis in that…

统计力学 · 物理学 2009-10-31 Bruce M. Boghosian

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical…

医学物理 · 物理学 2020-10-02 Aleksi Leino , Tuomas Lunttila , Meghdoot Mozumder , Aki Pulkkinen , Tanja Tarvainen

On the base of Diffusion Monte-Carlo method it is developed a new Complex Diffusion Monte-Carlo (CDMC) method allowing to simulate the quantum systems with complex wave function. There are no approximations on the calculation of modulus and…

凝聚态物理 · 物理学 2007-05-23 B. Abdullaev , M. Musakhanov , A. Nakamura

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…

强关联电子 · 物理学 2025-03-05 Weilun Jiang , Xiaofan Luo , Bin-Bin Mao , Zheng Yan

We perform a comprehensive analysis of the quantum-enhanced Monte Carlo method [Nature, 619, 282-287 (2023)], aimed at identifying the optimal working point of the algorithm. We observe an optimal mixing Hamiltonian strength and analyze the…

量子物理 · 物理学 2025-07-01 Johannes Christmann , Petr Ivashkov , Mattia Chiurco , Guglielmo Mazzola

This review covers applications of quantum Monte Carlo methods to quantum mechanical problems in the study of electronic and atomic structure, as well as applications to statistical mechanical problems both of static and dynamic nature. The…

chem-ph · 物理学 2016-10-26 M. P. Nightingale , C. J. Umrigar

Quantum sensing encompasses highly promising techniques with diverse applications including noise-reduced imaging, super-resolution microscopy as well as imaging and spectroscopy in challenging spectral ranges. These detection schemes use…

量子物理 · 物理学 2022-11-23 Felix Riexinger , Mirco Kutas , Björn Haase , Michael Bortz , Georg von Freymann

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,…

In this work, we introduce a simple modification of the Monte Carlo algorithm, which we call step Monte Carlo (sMC). The sMC approach allows to simulate processes far from equilibrium and obtain information about the dynamic properties of…

其他凝聚态物理 · 物理学 2023-12-15 Dariusz Sztenkiel

Many experimentally-accessible, finite-sized interacting quantum systems are most appropriately described by the canonical ensemble of statistical mechanics. Conventional numerical simulation methods either approximate them as being coupled…

强关联电子 · 物理学 2023-05-23 Tong Shen , Hatem Barghathi , Jiangyong Yu , Adrian Del Maestro , Brenda Rubenstein