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Quantum Monte Carlo (QMC) methods represent a powerful family of computational techniques for tackling complex quantum many-body problems and performing calculations of stationary state properties. QMC is among the most accurate and…

材料科学 · 物理学 2025-01-08 Alfonso Annarelli , Dario Alfè , Andrea Zen

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.…

强关联电子 · 物理学 2016-12-08 Mingpu Qin , Hao Shi , Shiwei Zhang

Detector response to a high-energy physics process is often estimated by Monte Carlo simulation. For purposes of data analysis, the results of this simulation are typically stored in large multi-dimensional histograms, which can quickly…

数据分析、统计与概率 · 物理学 2013-06-14 Nathan Whitehorn , Jakob van Santen , Sven Lafebre

Radial basis functions are a common mathematical tool used to construct a smooth interpolating function from a set of data points. A spatial prior based on thin-plate spline radial basis functions can be easily implemented resulting in a…

统计方法学 · 统计学 2019-06-14 Gentry White , Dongchu Sun , Paul Speckman

Silica is an abundant component of the Earth whose crystalline polymorphs play key roles in its structure and dynamics. As the simplest silicates, understanding pure silica is a prerequisite to understanding the rocky part of the Earth, its…

We describe the implementation of the frozen-orbital and downfolding approximations in the auxiliary-field quantum Monte Carlo (AFQMC) method. These approaches can provide significant computational savings compared to fully correlating all…

计算物理 · 物理学 2013-11-18 Wirawan Purwanto , Shiwei Zhang , Henry Krakauer

We present a many-body $GW$ formalism for quantum subsystems embedded in discrete polarizable environments containing up to several hundred thousand atoms described at a fully ab initio random phase approximation level. Our approach is…

化学物理 · 物理学 2024-01-26 David Amblard , Xavier Blase , Ivan Duchemin

We develop a formalism and present an algorithm for optimization of the trial wave-function used in fixed-node diffusion quantum Monte Carlo (DMC) methods. We take advantage of a basic property of the walker configuration distribution…

强关联电子 · 物理学 2015-05-13 Fernando A. Reboredo , Randolph Q. Hood , Paul R. C. Kent

Diffusion Monte Carlo is one of the most accurate scalable many-body methods for solid state systems. However, to date, spin-orbit interactions have not been incorporated into these calcualtions at a first-principles level; only having been…

材料科学 · 物理学 2020-03-04 Yueqing Chang , Lucas K. Wagner

Efficient and accurate algorithm for partition function, free energy and thermal entropy calculations is of great significance in statistical physics and quantum many-body physics. Here we present an unbiased but low-technical-barrier…

统计力学 · 物理学 2024-11-19 Yi-Ming Ding , Jun-Song Sun , Nvsen Ma , Gaopei Pan , Chen Cheng , Zheng Yan

Quantum Monte Carlo approaches such as the diffusion Monte Carlo (DMC) method are among the most accurate many-body methods for extended systems. Their scaling makes them well suited for defect calculations in solids. We review the various…

材料科学 · 物理学 2014-04-23 William D. Parker , John W. Wilkins , Richard G. Hennig

An efficient Path Integral Monte Carlo procedure is proposed to simulate the behavior of quantum many-body dissipative systems described within the framework of the influence functional. Thermodynamic observables are obtained by Monte Carlo…

统计力学 · 物理学 2009-11-07 Luca Capriotti , Alessandro Cuccoli , Andrea Fubini , Valerio Tognetti , Ruggero Vaia

Quantum Monte Carlo (QMC) methods are powerful approaches for solving electronic structure problems. Although they often provide high-accuracy solutions, the precision of most QMC methods is ultimately limited by a trial wave function that…

量子物理 · 物理学 2025-03-13 Nick S. Blunt , Laura Caune , Javiera Quiroz-Fernandez

For qubits, Monte Carlo estimation of the average fidelity of Clifford unitaries is efficient -- it requires a number of experiments that is independent of the number $n$ of qubits and classical computational resources that scale only…

量子物理 · 物理学 2014-10-23 Giulia Gualdi , David Licht , Daniel M. Reich , Christiane P. Koch

In this study we present an optimization method based on the quantum Monte Carlo diagonalization for many-fermion systems. Using the Hubbard-Stratonovich transformation, employed to decompose the interactions in terms of auxiliary fields,…

强关联电子 · 物理学 2009-11-13 Takashi Yanagisawa

We present, within Kohn-Sham Density Functional Theory calculations, a quantitative method to identify and assess the partitioning of a large quantum mechanical system into fragments. We then show how within this framework simple…

化学物理 · 物理学 2017-09-28 Stephan Mohr , Michel Masella , Laura E. Ratcliff , Luigi Genovese

A statistical method is derived for the calculation of thermodynamic properties of many-body systems at low temperatures. This method is based on the self-healing diffusion Monte Carlo method for complex functions [F. A. Reboredo J. Chem.…

其他凝聚态物理 · 物理学 2014-03-05 Fernando A. Reboredo , Jeongnim Kim

Compact and accurate wave functions can be constructed by quantum Monte Carlo methods. Typically, these wave functions consist of a sum of a small number of Slater determinants multiplied by a Jastrow factor. In this paper we study the…

凝聚态物理 · 物理学 2009-10-30 Chien-Jung Huang , C. J. Umrigar , M. P. Nightingale

We present a method to construct pseudo-BCS wave functions from the one-body density matrix. The resulting many-body wave function, which can be produced for any fermion systems, including those with purely repulsive interactions, has the…

强关联电子 · 物理学 2021-01-27 Zhiyu Xiao , Hao Shi , Shiwei Zhang

Exact simulations of quantum circuits (QCs) are currently limited to $\sim$50 qubits because the memory and computational cost required to store the QC wave function scale exponentially with qubit number. Therefore, developing efficient…

量子物理 · 物理学 2025-02-18 Marco Bernardi