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An inhomogeneous backflow transformation for many-particle wave functions is presented and applied to electrons in atoms, molecules, and solids. We report variational and diffusion quantum Monte Carlo VMC and DMC energies for various…

Computational Physics · Physics 2008-01-04 P. Lopez-Rios , A. Ma , N. D. Drummond , M. D. Towler , R. J. Needs

We consider the problem of computing an approximation to the integral $I=\int_{[0,1]^d}f(x) dx$. Monte Carlo (MC) sampling typically attains a root mean squared error (RMSE) of $O(n^{-1/2})$ from $n$ independent random function evaluations.…

Computation · Statistics 2008-11-05 Art B. Owen

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…

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…

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

We study signal processing tasks in which the signal is mapped via some generalized time-frequency transform to a higher dimensional time-frequency space, processed there, and synthesized to an output signal. We show how to approximate such…

Numerical Analysis · Mathematics 2021-09-07 Ron Levie , Haim Avron , Gitta Kutyniok

Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance of MCVI crucially depends on the variance of its stochastic…

Machine Learning · Statistics 2018-07-05 Alexander Buchholz , Florian Wenzel , Stephan Mandt

We extend the recently introduced phaseless auxiliary-field quantum Monte Carlo (QMC) approach to any single-particle basis, and apply it to molecular systems with Gaussian basis sets. QMC methods in general scale favorably with system…

Computational Physics · Physics 2007-05-23 W. A. Al-Saidi , Shiwei Zhang , Henry Krakauer

We develop an all-electron quantum Monte Carlo (QMC) method for solids that does not rely on pseudopotentials, and use it to construct a primary ultra-high pressure calibration based the equation of state of cubic boron nitride(c-BN). We…

Materials Science · Physics 2015-05-14 K. P. Esler , R. E. Cohen , B. Militzer , Jeongnim Kim , R. J. Needs , M. D. Towler

Certain point defects in solids can efficiently be used as qubits for applications in quantum technology. They have spin states that are initializable, readable, robust, and can be manipulated optically. New theoretical methods are needed…

Computational Physics · Physics 2023-09-20 Kristoffer Simula , Ilja Makkonen

Quantum Monte Carlo calculations of the first-row atoms Li-Ne and their singly-positively-charged ions are reported. Multi-determinant-Jastrow-backflow trial wave functions are used which recover more than 98% of the correlation energy at…

Computational Physics · Physics 2016-08-14 P. Seth , P. López Ríos , R. J. Needs

The accurate computation of forces and other energy derivatives has been a long-standing challenge for quantum Monte Carlo methods. A number of technical obstacles contribute to this challenge. We discuss how these obstacles can be removed…

Materials Science · Physics 2023-05-29 Siyuan Chen , Shiwei Zhang

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…

Quantum Physics · Physics 2025-03-13 Nick S. Blunt , Laura Caune , Javiera Quiroz-Fernandez

Because of their robustness, efficiency and non-intrusiveness, Monte Carlo methods are probably the most popular approach in uncertainty quantification to computing expected values of quantities of interest (QoIs). Multilevel Monte Carlo…

Numerical Analysis · Mathematics 2022-04-12 Marcus J. Grote , Simon Michel , Fabio Nobile

Variational Monte Carlo (VMC) methods are used to sample classically from distributions corresponding to quantum states which have an efficient classical description. VMC methods are based on performing a number of steps of a Markov chain…

Quantum Physics · Physics 2023-10-27 Ashley Montanaro , Stasja Stanisic

The Kinetic-Diffusion Monte Carlo (KDMC) method is a powerful tool for simulating neutral particles in fusion reactors. It is a hybrid fluid-kinetic method that is significantly faster than pure kinetic methods at the cost of a small bias…

Numerical Analysis · Mathematics 2025-09-05 Thijs Steel , Vince Maes , Giovanni Samaey

Variational and diffusion quantum Monte Carlo (VMC and DMC) calculations of the properties of the zero-temperature fermionic gas at unitarity are reported. The ratio of the energy of the interacting to the non-interacting gas for a system…

Quantum Gases · Physics 2010-03-26 Andrew J. Morris , P. Lopez Rios , R. J. Needs

In this paper, we propose a general analysis framework for inexact power iteration, which can be used to efficiently solve high dimensional eigenvalue problems arising from quantum many-body problems. Under the proposed framework, we…

Numerical Analysis · Mathematics 2018-06-29 Jianfeng Lu , Zhe Wang

We study the efficiency, precision and accuracy of all-electron variational and diffusion quantum Monte Carlo calculations using Slater basis sets. Starting from wave functions generated by Hartree-Fock and density functional theory, we…

Materials Science · Physics 2010-02-11 Norbert Nemec , Michael D. Towler , R. J. Needs

In the last few years we have been developing a Monte Carlo simulation method to cope with systems of many electrons and ions in the Born-Oppenheimer (BO) approximation, the Coupled Electron-Ion Monte Carlo Method (CEIMC). Electronic…

Computational Physics · Physics 2007-05-23 Carlo Pierleoni , David M. Ceperley
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