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Relevant bibliographies by topics / Quantum Monte Carlo

Academic literature on the topic 'Quantum Monte Carlo'

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Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Quantum Monte Carlo"

1

Mitas, Lubos. "Quantum Monte Carlo." Current Opinion in Solid State and Materials Science 2, no. 6 (December 1997): 696–700. http://dx.doi.org/10.1016/s1359-0286(97)80012-5.

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CEPERLEY, D., and B. ALDER. "Quantum Monte Carlo." Science 231, no. 4738 (February 7, 1986): 555–60. http://dx.doi.org/10.1126/science.231.4738.555.

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Bozkus, Zeki, Ahmad Anbar, and Tarek El-Ghazawi. "Adaptive Computing Library for Quantum Monte Carlo Simulations." International Journal of Computer Theory and Engineering 6, no. 3 (2014): 200–205. http://dx.doi.org/10.7763/ijcte.2014.v6.862.

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Kawashima, Naoki. "Quantum Monte Carlo Methods." Progress of Theoretical Physics Supplement 145 (2002): 138–49. http://dx.doi.org/10.1143/ptps.145.138.

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Reynolds, Peter J., Jan Tobochnik, and Harvey Gould. "Diffusion Quantum Monte Carlo." Computers in Physics 4, no. 6 (1990): 662. http://dx.doi.org/10.1063/1.4822960.

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Wang, Yazhen. "Quantum Monte Carlo simulation." Annals of Applied Statistics 5, no. 2A (June 2011): 669–83. http://dx.doi.org/10.1214/10-aoas406.

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Lüchow, Arne. "Quantum Monte Carlo methods." Wiley Interdisciplinary Reviews: Computational Molecular Science 1, no. 3 (April 11, 2011): 388–402. http://dx.doi.org/10.1002/wcms.40.

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The Lam, Nguyen. "QUANTUM DIFFUSION MONTE CARLO METHOD FOR LOW-DIMENTIONAL SYSTEMS." Journal of Science, Natural Science 60, no. 7 (2015): 81–87. http://dx.doi.org/10.18173/2354-1059.2015-0036.

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Doll, J. D., Steven W. Rick, and David L. Freeman. "Stationary phase Monte Carlo methods: interference effects in quantum Monte Carlo dynamics." Canadian Journal of Chemistry 70, no. 2 (February 1, 1992): 497–505. http://dx.doi.org/10.1139/v92-071.

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As summarized by Hamming's motto, "the purpose of computing is insight, not numbers." In the spirit of this dictum, we describe here recent algorithmic developments in the theory of quantum dynamics. Through the use of the somewhat unlikely combination of modern numerical simulations and a visualization device borrowed from 19th century optics, the present efforts suggest the existence of an important, underlying structure in the general problem. This structure, verified for a relatively simple class of model problems, provides broad guidelines for the Keywords: Monte Carlo, stationary phase, quantum dynamics.

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Grossman, Jeffrey C. "Benchmark quantum Monte Carlo calculations." Journal of Chemical Physics 117, no. 4 (July 22, 2002): 1434–40. http://dx.doi.org/10.1063/1.1487829.

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Dissertations / Theses on the topic "Quantum Monte Carlo"

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Badinski, Alexander Nikolai. "Forces in quantum Monte Carlo." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612494.

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Hine, Nicholas. "New applications of quantum Monte Carlo." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446023.

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Poole, Thomas. "Calculating derivatives within quantum Monte Carlo." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/29359.

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Quantum Monte Carlo (QMC) methods are powerful, stochastic techniques for computing the properties of interacting electrons and nuclei with an accuracy comparable to the standard post-Hartree--Fock methods of quantum chemistry. Whilst the favourable scaling of QMC methods enables a quantum, many-body treatment of much larger systems, the lack of accurate and efficient total energy derivatives, required to compute atomic forces, has hindered their widespread adoption. The work contained within this thesis provides an efficient procedure for calculating exact derivatives of QMC results. This procedure uses the programming technique of algorithmic differentiation (AD), which allows access to the derivatives of a computed function by applying chain rule differentiation to the underlying source code. However, this thesis shows that a straightforward differentiation of a stochastic function fails to capture the important contribution to the derivative from probabilistic decisions. A general approach for calculating the derivatives of a stochastic function is presented, where a similar adaptation of AD applied to the diffusion Monte Carlo (DMC) algorithm yields exact DMC atomic forces. The approach is validated by performing the largest ever DMC force calculations, which demonstrate the feasibility of treating systems containing thousands of electrons. The efficiency of AD also enables molecular dynamics simulations driven entirely by DMC, adding new functionality to the QMC toolkit. Another focus of this thesis is using the phenomenon of stochastic coherence to correlate DMC simulations, allowing finite difference derivatives to be obtained with a small error. Whilst this method is far easier to implement than AD, preliminary results show an instability when treating larger systems. A different approach is obtained from extrapolating this method to a finite difference step size of zero, producing algebraic expressions for a direct differentiation of the DMC algorithm.

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Seth, Priyanka. "Improved wave functions for quantum Monte Carlo." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/244333.

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Quantum Monte Carlo (QMC) methods can yield highly accurate energies for correlated quantum systems. QMC calculations based on many-body wave functions are considerably more accurate than density functional theory methods, and their accuracy rivals that of the most sophisticated quantum chemistry methods. This thesis is concerned with the development of improved wave function forms and their use in performing highly-accurate quantum Monte Carlo calculations. All-electron variational and diffusion Monte Carlo (VMC and DMC) calculations are performed for the first-row atoms and singly-positive ions. Over 98% of the correlation energy is retrieved at the VMC level and over 99% at the DMC level for all the atoms and ions. Their first ionization potentials are calculated within chemical accuracy. Scalar relativistic corrections to the energies, mass-polarization terms, and one- and two-electron expectation values are also evaluated. A form for the electron and intracule densities is presented and fits to this form are performed. Typical Jastrow factors used in quantum Monte Carlo calculations comprise electron-electron, electron-nucleus and electron-electron-nucleus terms. A general Jastrow factor capable of correlating an arbitrary of number of electrons and nuclei, and including anisotropy is outlined. Terms that depend on the relative orientation of electrons are also introduced and applied. This Jastrow factor is applied to electron gases, atoms and molecules and is found to give significant improvement at both VMC and DMC levels. Similar generalizations to backflow transformations will allow useful additional variational freedom in the wave function. In particular, the use of different backflow functions for different orbitals is expected to be important in systems where the orbitals are qualitatively different. The modifications to the code necessary to accommodate orbital-dependent backflow functions are described and some systems in which they are expected to be important are suggested.

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Leung, Wing-Kai. "Applications of continuum quantum Monte Carlo methods." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.411231.

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Brown, M. D. "Energy minimisation in variational quantum Monte Carlo." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596975.

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After reviewing previously published techniques, a new algorithm is presented for optimising variable parameters in explicitly correlated many-body trial wavefunctions for use in variational quantum Monte Carlo (VMC) and diffusion quantum Monte Carlo (DMC) calculations. The method optimises the parameters with respect to the VMC energy by extending a low-noise VMC implementation of diagonalisation to include parameters which affect the wavefunction to higher than first-order. Similarly to minimising the variance of the local energy by fixed-sampling, accurate results are achieved using a relatively small number of VMC configurations because the optimisation is based on a least-squares fitting procedure. The method is tested by optimising six small examples intended to broadly cover the range of systems and wavefunctions typically treated using VMC and DMC, including atoms, molecules, and extended systems. Least-squares energy minimisation is found to be stable, fast enough to be practical, and capable of achieving lower VMC energies than minimisation of the filtered underweighted variance of the local energy (and the underweighted mean absolute deviation from the median local energy) by fixed-sampling. Least-squares energy minimisation is used to optimise four different wavefunctions for each of the all-electron first row atoms, from lithium to neon: single-determinant Slater-Jastrow wavefunctions with and without backflow transformations, and multi-determinant Slater-Jastrow wavefunctions with and without backflow transformations. The optimisations are more stable and successful than some previous variance minimisations using similar wavefunctions. The DMC energies of the energy-optimised wavefunctions for the atoms from boron to neon are significantly lower than previously published results, and, using the multi-determinant Slater-Jastrow wavefunctions with backflow, the calculations recover at least 90% of the correlation energies for lithium, beryllium, boron, carbon, nitrogen and neon, 97% for oxygen, and 98% for fluorine.

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Williamson, Andrew James. "Quantum Monte Carlo calculations of electronic excitations." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627604.

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Kent, Paul Richard Charles. "Techniques and applications of quantum Monte Carlo." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624448.

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Kunert, Roland. "Monte Carlo simulation of stacked quantum dot arrays." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=981321399.

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Gillies, Patrick R. "Path integral quantum Monte Carlo for semiconductor nanostructures." Thesis, Heriot-Watt University, 2007. http://hdl.handle.net/10399/2033.

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Path integral quantum Monte Carlo (PI-QMC) is a powerful technique, which can be used to model the properties of multiple interacting particles at finite temperatures. In this work path integral quantum Monte Carlo has been applied to the problem of few particle interactions in quantum dots and other semiconductor nanostructures. Quantum dots are currently the subject of much research and in order to further understand their properties it is necessary to perform theoretical modelling. In this work, the method by which the problem of the attractive Coulomb potential was overcome is detailed. Following that, comparisons are made between . experimental data and PI-QMC results for excitonic complexes in 111-V dots. Both the energies and voltage extents were found to show good agreement between experiment and theory. Comparisons are also between theory and experiment of II-VI, with experimental data using a harmonic potential to model the dot. Again, good agreement is seen. Finally, as an example of the power of PI-QMC, the behaviour of electrons and holes is modelled for alternative nanostructures, such as coupled quantum dots, quantum rings and core-shell structures. With some simple modifications, the same PI-QMC method could be used.

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Books on the topic "Quantum Monte Carlo"

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Schattke, Wolfgang, and Ricardo Díez Muiño. Quantum Monte Carlo Programming. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527676729.

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American Chemical Society. Division of Physical Chemistry, ed. Advances in quantum Monte Carlo. Washington, DC: American Chemical Society, 2012.

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1935-, Anderson James B., and Rothstein Stuart M, eds. Advances in quantum Monte Carlo. Washington, DC: American Chemical Society, 2007.

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Anderson, James B., and Stuart M. Rothstein, eds. Advances in Quantum Monte Carlo. Washington, DC: American Chemical Society, 2006. http://dx.doi.org/10.1021/bk-2007-0953.

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Tanaka, Shigenori, Stuart M. Rothstein, and William A. Lester, eds. Advances in Quantum Monte Carlo. Washington, DC: American Chemical Society, 2012. http://dx.doi.org/10.1021/bk-2012-1094.

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1935-, Anderson James B., ed. Quantum Monte Carlo: Origins, development, applications. New York: Oxford University Press, 2006.

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Rubenstein, Brenda M. Novel Quantum Monte Carlo Approaches for Quantum Liquids. [New York, N.Y.?]: [publisher not identified], 2013.

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Tanaka, Shigenori, Pierre-Nicholas Roy, and Lubos Mitas, eds. Recent Progress in Quantum Monte Carlo. Washington, DC: American Chemical Society, 2016. http://dx.doi.org/10.1021/bk-2016-1234.

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P, Nightingale M., Umrigar C. J, and NATO Advanced Study Institute on Quantum Monte Carlo Methods in Physics and Chemistry (1998 : Ithaca, N.Y.), eds. Quantum Monte Carlo methods in physics and chemistry. Dordrecht: Kluwer Academic, 1999.

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Henryk, Woźniakowski, and SpringerLink (Online service), eds. Monte Carlo and Quasi-Monte Carlo Methods 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Quantum Monte Carlo"

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Scalapino, D. J. "Quantum Monte Carlo." In Springer Series in Solid-State Sciences, 194–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83033-4_20.

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DISSERTORI, GÜNTHER, IAN G. KNOWLES, and MICHAEL SCHMELLING. "Monte Carlo Models." In Quantum Chromodynamics, 179–205. Oxford University Press, 2009. http://dx.doi.org/10.1093/acprof:oso/9780199566419.003.0004.

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Finnila, A. B., M. A. Gomez, C. Sebenik, C. Stenson, and J. D. Doll. "Quantum annealing: A new method for minimizing multidimensional functions." In Quantum Monte Carlo, 92. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.0095.

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Abstract Quantum Monte Carlo has found a new application far afield from the solution of the Schrodinger equation for many-body problems. In this paper the authors report a method for solving multidimensional optimization problems that is superior, in many cases, to other methods such as conjugate gradient methods, the simplex method, direction-set methods, genetic methods, and classical simulated annealing. The new approach, aptly titled “quantum annealing,” is closely related to classical annealing, in which a physical system such as a metal cluster is slowly cooled and finds its lowest-energy configuration as the temperature drops to zero. In classical annealing the system follows classical-mechanical dynamics as the temperature drops. In quantum annealing a collection of configurations evolves in the the same way as walkers undergoing diffusion and multiplication in diffusion QMC with the added feature of a slowly dropping value for Planck’s constant h. Classical annealing allows the escape from local minima with the aid of thermal fluctuations. Quantum annealing allows such escape by delocalization and tunneling. Since a QMC walker can multiply in number on entering a low-energy region, a single walker can populate an entire region.

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Metropolis, N., and S. Ulam. "The Monte Carlo method." In Quantum Monte Carlo, 2. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.0002.

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Abstract In this paper Metropolis and Ulam gave a brief introduction to “the Monte Carlo method” which is described as a statistical approach to the study of differential equations as applied by Metropolis, Ulam, Fermi, von Neumann, Feynman, and others at the Los Alamos Laboratory in the 1940s.0 Several examples of applications of Monte Carlo calculations are given. These include predicting the probability of winning at the game of solitaire, calculating the volume of an irregular region in high-dimensional space, and solving the Fokker-Planck equation for diffusion and multiplication of nuclear particles. The paper is completed with the first published description of the diffusion quantum Monte Carlo method, which is attributed to a suggestion by Fermi. In their words, “... the time-independent Schrödinger equation could be studied as follows. Re-introduce time dependence by considering This last equation can be interpreted however as describing the behavior of a system of particles each of which performs a random walk, i.e., diffuses isotropically and at the same time is subject to multiplication, which is determined by the point value of V.” The authors then indicate that the procedure gives the wavefunction corresponding to the lowest eigenvalue E.

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Conroy, H. "Molecular Schrödinger equation. II. Monte Carlo evaluation of integrals." In Quantum Monte Carlo, 4. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.0004.

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Abstract This paper and its three companion papersa published back-to-back in the Journal of Chemical Physics describe the first variational QMC calculations for molecular systems. The first paper introduces a general form for a one-electron wavefunction along with discussions of the requirements for an accurate wavefunction and the procedure for optimization by minimizing the variance in local energies. The second, with the title given above, describes the Monte Carlo evaluation of the matrix elements required for determination of the expectation value of the energy in a variational calculation. As pointed out, Monte Carlo schemes had often been used for integrations, but there was apparently no prior report of such schemes for problems in quantum mechanics. The need for selecting points from an approximate ψ2 distribution was recognized and a procedure for doing so was proposed. In this paper and in the third and fourth papers the methods were illustrated with applications for the systems H2 +, HeH2+, HeH+, H3 2+, and H3 +. In the case of H3 + sections of potential energy surfaces were determined for linear and triangular nuclear configurations using wavefunctions with up to 29 terms. The minimum-energy structure for H3 + was found to be an equilateral triangle of side length 1.68 bohr with an energy of -1.357 hartrees, values more accurate than those of any prior calculations.

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Anderson, J. B. "A random-walk simulation of the Schrödinger equation:H+ 3." In Quantum Monte Carlo, 10. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.0010.

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Abstract This paper opened the field of electronic structure calculations to quantum Monte Carlo calculations of the form originally suggested by Fermia. The system treated was the molecular ion H+ 3 which has served as a test case for new methods in quantum mechanics since about 1935. With the three protons fixed in position the problem is reduced to that of two electrons in three dimensions each. For the ground state the electrons (fermions) have opposite spins and the wavefunction is nodeless. In atomic units the Schrödinger equation in imaginary time becomes The energy obtained for the equilibrium equilateral triangle nuclear configuration was lower (but with an overlapping error bar) than the lowest-energy analytic variational result at the time and in agreement with recent predictions. The author discussed several of the questions bearing on the utility of the method, proposed the fixed-node method for incorporating the Pauli exclusion principle, and concluded that “‘In general, not enough is known for confident assessment of the merits of the method, but initial results are encouraging.”

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Baroni, S., and S. Moroni. "Reptation quantum Monte Carlo: A method for unbiased ground-state averages and imaginary-time correlations." In Quantum Monte Carlo, 117. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.00120.

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Abstract This paper introduces a successful new method for extending diffusion QMC with importance sampling to allow the direct calculation of pure ‘¢ 2 distributions in place of mixed ‘¢ 1/Jr distributions. The method removes any bias introduced with a trial function (except that due to node locations) in the calculation of observable quantities which do not commute with the Hamiltonian. It has the advantage, relative to the descendent weighting or forward walking technique,a of a controlled walker population which reduces statistical errors. The authors have named it the reptation quantum Monte Carlo (RQMC) method. Its basic object is a path or sequence of configurations called a “reptile” which may be altered by an action called “reptation,” terms adopted from related work in the polymer area. In conventional diffusion QMC with importance sampling walkers are subjected to diffusion, drift, and a local energy-based multiplication to produce a 1/J’I/Jr distribution. Without multiplication a ‘l/Jr’l/Jr distribution is produced. In RQMC multiplication is replaced by a Metropolis rejection procedure, also based on local energies, which produces a 1/J’!/J distribution of configurations in a reptile. The generation of a new reptile, by removing the tail and adding a new head, is based on diffusion and drift for proposed moves coupled with an acceptance test. Overall, the implementation is quite similar to that for variational QMC. As described, the method was tested first for the hydrogen atom, followed by the successful demonstration of a calculation for a helium fluid, 64 4He atoms in a cubic box with periodic boundary conditions. The advantages of RQMC relative to branching diffusion QMC were pointed out for treatment of clusters, films, and superfluids as well as for estimation of electronic forces.

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Chen, B., and J. B. Anderson. "Improved quantum Monte Carlo calculation of the ground-state energy of the hydrogen molecule." In Quantum Monte Carlo, 96. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.0099.

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Abstract Since the ground state of the hydrogen molecule is nodeless, it is easily treated by “exact” QMC cancellation methods in a full 12-dimensional, four-particle coordinate space to yield the groundstate energy without the use of the Born-Oppenheimer approximation. Following the discovery of quantum mechanics, the hydrogen molecule has been the frequent target of theoretical predictions of increasing accuracy, in parallel with experimental measurements of its ionization potential and its dissociation energy, also with increasing accuracy. The calculations reported in this paper took advantage of importance sampling and efficient cancellation to yield an accuracy of better than 1 microhartree in the total energy, with the result of -1.164 0237 ± 0.000 0009 hartrees, a value slightly less accurate than analytic variational calculations at the time. Expressed as the dissociation energy and corrected for relativistic and radiative effects, the QMC result in 36117.84 ± 0.20 cm-1, which may be compared with the experimental valuea of 36118.11 cm-1 determined in 1992.

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Manten, S., and A. Lüchow. "On the accuracy of the fixed-node diffusion quantum Monte Carlo method." In Quantum Monte Carlo, 130. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.00133.

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Abstract In this work Manten and Liichow assessed the accuracy of allelectron fixed-node diffusion QMC calculations with nodes taken from Hartee-Fock wavefunctions with high-quality basis sets. The electronic energies associated with 17 different reactions involving a total of 20 small molecules were determined and compared with experimental values and with those calculated by Klopper et al.a using the coupled cluster method CCSD(T) with correlation consistent basis sets cc-pVDZ and cc-pVTZ. The molecules involved were limited to those containing the atoms H, C, 0, N, and F. Typical reactions were H2CO + 2H2 --+ CH4 + H2O and H2O + F --+ HOF + H. The geometries for the QMC calculations were optimized with MP2/cc-pVTZ calculations. The QMC total energies for the 20 molecules were lower than CCSD(T) values, but only valence electrons were correlated in the CCSD(T) calculations. Node location error was approximately 0.0015 hartrees for the C, N, 0, and F atoms and their hydrides, but substantial cancellation of node error is to be expected in determining enthalpy changes for reactions.

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Reynolds, P. J., D. M. Ceperley, B. J. Alder, and W. A. Lester. "Fixed-node quantum Monte Carlo for molecules." In Quantum Monte Carlo, 25. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.0026.

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Abstract This paper extends earlier calculations for the molecules H2 and LiH, and it increases the range to six and ten electrons with calculations for Li2 and H2O. The calculations were carried out with importance sampling using trial functions consisting of Slater determinants multiplied by Jastrow factors. For each system the expectation values of the energies for the trial functions were somewhat higher than those of the best configuration interaction calculations available at the time, but the fixed-node diffusion energies were significantly lower. The observed energy obtained for H2 was -1.174 ± 0.001 hartrees, compared to earlier values of -1.1744 hartrees in analytic variational calculations. For H2O it was - 76.377 ± 0.007 hartrees, compared to a value of - 76.3683 hartrees from variational calculations and an estimated true value for H20 of - 76.438 hartrees. The difference between the QMC value and the true value illustrates the problem of inaccurate node locations, in this case corresponding to a node location error of about 38 kcal/mol. Nevertheless, the energy so determined was about 5 kcal/mol lower than that of the lowest energy analytic variational calculation of the time.

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Conference papers on the topic "Quantum Monte Carlo"

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Troyer, Matthias, Philipp Werner, Adolfo Avella, and Ferdinando Mancini. "Quantum Monte Carlo Simulations." In LECTURES ON THE PHYSICS OF STRONGLY CORRELATED SYSTEMS XIII: Thirteenth Training Course in the Physics of Strongly Correlated Systems. AIP, 2009. http://dx.doi.org/10.1063/1.3225490.

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Isaacson, Joshua, William Jay, Alessandro Lovato, Pedro Machado, and Noemi Rocco. "Quantum Monte Carlo Based Approach to Intranuclear Cascades." In Quantum Monte Carlo Based Approach to Intranuclear Cascades. US DOE, 2020. http://dx.doi.org/10.2172/1827264.

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Zhang, Shiwei, and M. H. Kalos. "Exact Monte Carlo for few-electron systems." In Computational quantum physics. AIP, 1992. http://dx.doi.org/10.1063/1.42615.

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FANTONI, STEFANO, ANTONIO SARSA, and KEVIN E. SCHMIDT. "QUANTUM MONTE CARLO FOR NUCLEAR ASTROPHYSICS." In Proceedings of a Meeting Held in the Framework of the Activities of GISELDA, the Italian Working Group on Strong Interactions. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776532_0012.

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Oriols, X. "Monte Carlo Simulation of Quantum Noise." In NOISE AND FLUCTUATIONS: 18th International Conference on Noise and Fluctuations - ICNF 2005. AIP, 2005. http://dx.doi.org/10.1063/1.2036861.

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Pandharipande, V. R. "Quantum Monte Carlo calculations of nuclei." In Bates 25: celebrating 25 years of beam to experiment. AIP, 2000. http://dx.doi.org/10.1063/1.1291499.

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Geiger, Klaus, and Berndt Müller. "Quark-gluon transport theory: A Monte-Carlo simulation." In Computational quantum physics. AIP, 1992. http://dx.doi.org/10.1063/1.42601.

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COLLETTI, L., F. PEDERIVA, E. LIPPARINI, and C. J. UMRIGAR. "POLARIZABILITY IN QUANTUM DOTS VIA CORRELATED QUANTUM MONTE CARLO." In Proceedings of the 14th International Conference. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812779885_0028.

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Pieper, Steven C. "Quantum Monte Carlo calculations of light nuclei." In EXOTIC NUCLEI AND ATOMIC MASSES. ASCE, 1998. http://dx.doi.org/10.1063/1.57240.

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Neuber, Danilo R. "Data Analysis for Quantum Monte Carlo Simulations." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835219.

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Reports on the topic "Quantum Monte Carlo"

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Brown, W. R. Quantum Monte Carlo for vibrating molecules. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/414375.

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Williams, Timothy J., Ramesh Balakrishnan, Steven C. Pieper, Alessandro Lovato, Ewing Lusk, Maria Piarulli, and Robert Wiringa. Quantum Monte Carlo Calculations in Nuclear Theory. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1483999.

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David Ceperley. Quantum Monte Carlo Endstation for Petascale Computing. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/1007216.

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Wiringa, R. B. Quantum Monte Carlo calculations for light nuclei. Office of Scientific and Technical Information (OSTI), October 1997. http://dx.doi.org/10.2172/554896.

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Mitas, Lubos. Quantum Monte Carlo Endstation for Petascale Computing. Office of Scientific and Technical Information (OSTI), January 2011. http://dx.doi.org/10.2172/1003876.

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Barnett, R. N. Quantum Monte Carlo for atoms and molecules. Office of Scientific and Technical Information (OSTI), November 1989. http://dx.doi.org/10.2172/7040202.

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Engelhardt, Larry. Quantum Monte Carlo Calculations Applied to Magnetic Molecules. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/892729.

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Ashok Srinivasan. Random Number Generation for Petascale Quantum Monte Carlo. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/973573.

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Owen, Richard Kent. Quantum Monte Carlo methods and lithium cluster properties. Office of Scientific and Technical Information (OSTI), December 1990. http://dx.doi.org/10.2172/10180548.

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Mei-Yin Chou. Quantum Monte-Carlo Study of Electron Correlation in Heterostructure Quantum Dots. Office of Scientific and Technical Information (OSTI), November 2006. http://dx.doi.org/10.2172/894945.

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