Results 41 to 50 of about 926,631 (217)
Liquid State of One-Dimensional Bose Mixtures: A Quantum Monte Carlo Study. [PDF]
Physical Review Letters, 2018 By using exact quantum Monte Carlo methods we calculate the ground-state properties of the liquid phase in one-dimensional Bose mixtures with contact interactions.
Luca Parisi +2 more
semanticscholar +1 more source
Ab initio computations of molecular systems by the auxiliary‐field quantum Monte Carlo method [PDF]
, 2017 The auxiliary‐field quantum Monte Carlo (AFQMC) method provides a computational framework for solving the time‐independent Schrödinger equation in atoms, molecules, solids, and a variety of model systems.
M. Motta, Shiwei Zhang
semanticscholar +1 more source
Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems [PDF]
, 2022 We propose a quantum Monte Carlo approach to solve the ground state many-body Schrodinger equation for the electronic ground state. The method combines optimization from variational Monte Carlo and propagation from auxiliary field quantum Monte Carlo, in a way that significantly alleviates the sign problem.
arxiv +1 more source
Ab Initio Finite Temperature Auxiliary Field Quantum Monte Carlo. [PDF]
Journal of Chemical Theory and Computation, 2018 We present an ab initio auxiliary field quantum Monte Carlo method for studying the electronic structure of molecules, solids, and model Hamiltonians at finite temperature. The algorithm marries the ab initio phaseless auxiliary field quantum Monte Carlo
Yuan Liu, Minsik Cho, B. Rubenstein
semanticscholar +1 more source
Ab Initio Quantum Monte Carlo Simulation of the Warm Dense Electron Gas in the Thermodynamic Limit. [PDF]
Physical Review Letters, 2016 We perform ab initio quantum Monte Carlo (QMC) simulations of the warm dense uniform electron gas in the thermodynamic limit. By combining QMC data with the linear response theory, we are able to remove finite-size errors from the potential energy over ...
T. Dornheim +5 more
semanticscholar +1 more source
Diffusion Monte Carlo approach versus adiabatic computation for local Hamiltonians [PDF]
Phys. Rev. A 97, 022323 (2018), 2017 Most research regarding quantum adiabatic optimization has focused on stoquastic Hamiltonians, whose ground states can be expressed with only real, nonnegative amplitudes. This raises the question of whether classical Monte Carlo algorithms can efficiently simulate quantum adiabatic optimization with stoquastic Hamiltonians.
arxiv +1 more source
From Monte Carlo to quantum computation [PDF]
Mathematics and Computers in Simulation, 2003 Quantum computing was so far mainly concerned with discrete problems. Recently, E. Novak and the author studied quantum algorithms for high dimensional integration and dealt with the question, which advantages quantum computing can bring over classical deterministic or randomized methods for this type of problem.
openaire +2 more sources
Electronic structure quantum Monte Carlo [PDF]
Acta Physica Slovaca. Reviews and Tutorials, 2009 Electronic structure quantum Monte CarloQuantum Monte Carlo (QMC) is an advanced simulation methodology for studies of manybody quantum systems. The QMC approaches combine analytical insights with stochastic computational techniques for efficient solution of several classes of important many-body problems such as the stationary Schrödinger equation ...
Michal Bajdich, Lubos Mitas
openaire +3 more sources
Quantum Monte Carlo calculations of energy gaps from first principles [PDF]
Physical review B, 2018 We review the use of continuum quantum Monte Carlo (QMC) methods for the calculation of energy gaps from first principles, and present a broad set of excited-state calculations carried out with the variational and fixed-node diffusion QMC methods on ...
R. Hunt +4 more
semanticscholar +1 more source
Quantum Quasi-Monte Carlo Technique for Many-Body Perturbative Expansions. [PDF]
Physical Review Letters, 2020 High order perturbation theory has seen an unexpected recent revival for controlled calculations of quantum many-body systems, even at strong coupling. We adapt integration methods using low-discrepancy sequences to this problem.
Marjan Maček +5 more
semanticscholar +1 more source