Bilinear Quantum Monte Carlo: Expectations and Energy Differences

We propose a bilinear sampling algorithm in Green's function Monte Carlo for expectation values of operators that do not commute with the Hamiltonian and for differences between eigenvalues of different Hamiltonians. The integral representations of the Schroedinger equations are transformed into two equations whose solution has the form $\psi_a(x) t(x,y) \psi_b(y)$, where $\psi_a$ and $\psi_b$ are the wavefunctions for the two related systems and $t(x,y)$ is a kernel chosen to couple $x$ and $y$. The Monte Carlo process, with random walkers on the enlarged configuration space $x \otimes y$, solves these equations by generating densities whose asymptotic form is the above bilinear distribution. With such a distribution, exact Monte Carlo estimators can be obtained for the expectation values of quantum operators and for energy differences. We present results of these methods applied to several test problems, including a model integral equation, and the hydrogen atom.Comment: 27 page

An Exact Monte Carlo Method for Continuum Fermion Systems

We offer a new proposal for the Monte Carlo treatment of many-fermion systems in continuous space. It is based upon Diffusion Monte Carlo with significant modifications: correlated pairs of random walkers that carry opposite signs; different functions ``guide'' walkers of different signs; the Gaussians used for members of a pair are correlated; walkers can cancel so as to conserve their expected future contributions. We report results for free-fermion systems and a fermion fluid with 14 $^3$He atoms, where it proves stable and correct. Its computational complexity grows with particle number, but slowly enough to make interesting physics within reach of contemporary computers.Comment: latex source, 3 separated figures (2 in jpg format, 1 in eps format

Multidimensional integration in a heterogeneous network environment

We consider several issues related to the multidimensional integration using a network of heterogeneous computers. Based on these considerations, we develop a new general purpose scheme which can significantly reduce the time needed for evaluation of integrals with CPU intensive integrands. This scheme is a parallel version of the well-known adaptive Monte Carlo method (the VEGAS algorithm), and is incorporated into a new integration package which uses the standard set of message-passing routines in the PVM software system.Comment: 19 pages, latex, 5 postscript figures include

Decisions, Decisions: Noise and its Effects on Integral Monte Carlo Algorithms

In the present paper we examine the effects of noise on Monte Carlo algorithms, a problem raised previously by Kennedy and Kuti (Phys. Rev. Lett. {\bf 54}, 2473 (1985)). We show that the effects of introducing unbiased noise into the acceptance/rejection phase of the conventional Metropolis approach are surprisingly modest, and, to a significant degree, largely controllable. We present model condensed phase numerical applications to support these conclusions.Comment: Chemical Physics Letters, 12 pages text, 5 figure

Entropic effects in large-scale Monte Carlo simulations

The efficiency of Monte Carlo samplers is dictated not only by energetic effects, such as large barriers, but also by entropic effects that are due to the sheer volume that is sampled. The latter effects appear in the form of an entropic mismatch or divergence between the direct and reverse trial moves. We provide lower and upper bounds for the average acceptance probability in terms of the Renyi divergence of order 1/2. We show that the asymptotic finitude of the entropic divergence is the necessary and sufficient condition for non-vanishing acceptance probabilities in the limit of large dimensions. Furthermore, we demonstrate that the upper bound is reasonably tight by showing that the exponent is asymptotically exact for systems made up of a large number of independent and identically distributed subsystems. For the last statement, we provide an alternative proof that relies on the reformulation of the acceptance probability as a large deviation problem. The reformulation also leads to a class of low-variance estimators for strongly asymmetric distributions. We show that the entropy divergence causes a decay in the average displacements with the number of dimensions n that are simultaneously updated. For systems that have a well-defined thermodynamic limit, the decay is demonstrated to be n^{-1/2} for random-walk Monte Carlo and n^{-1/6} for Smart Monte Carlo (SMC). Numerical simulations of the LJ_38 cluster show that SMC is virtually as efficient as the Markov chain implementation of the Gibbs sampler, which is normally utilized for Lennard-Jones clusters. An application of the entropic inequalities to the parallel tempering method demonstrates that the number of replicas increases as the square root of the heat capacity of the system.Comment: minor corrections; the best compromise for the value of the epsilon parameter in Eq. A9 is now shown to be log(2); 13 pages, 4 figures, to appear in PR

The Fermion Monte Carlo revisited

In this work we present a detailed study of the Fermion Monte Carlo algorithm (FMC), a recently proposed stochastic method for calculating fermionic ground-state energies [M.H. Kalos and F. Pederiva, Phys. Rev. Lett. vol. 85, 3547 (2000)]. A proof that the FMC method is an exact method is given. In this work the stability of the method is related to the difference between the lowest (bosonic-type) eigenvalue of the FMC diffusion operator and the exact fermi energy. It is shown that within a FMC framework the lowest eigenvalue of the new diffusion operator is no longer the bosonic ground-state eigenvalue as in standard exact Diffusion Monte Carlo (DMC) schemes but a modified value which is strictly greater. Accordingly, FMC can be viewed as an exact DMC method built from a correlated diffusion process having a reduced Bose-Fermi gap. As a consequence, the FMC method is more stable than any transient method (or nodal release-type approaches). We illustrate the various ideas presented in this work with calculations performed on a very simple model having only nine states but a full sign problem. Already for this toy model it is clearly seen that FMC calculations are inherently uncontrolled.Comment: 49 pages with 4 postscript figure

A Monte-Carlo Approach to Zero Energy Quantum Scattering

Monte-Carlo methods for zero energy quantum scattering are developed. Starting from path integral representations for scattering observables, we present results of numerical calculations for potential scattering and scattering off a schematic $^4 \rm He$ nucleus. The convergence properties of Monte-Carlo algorithms for scattering systems are analyzed using stochastic differential equation as a path sampling method.Comment: 30 pages, LaTeX, 8 (uuencoded, tared and gziped) postscript figure

Fermionic Shadow Wavefunction Variational calculations of the vacancy formation energy in 3^3He

We present a novel technique well suited to study the ground state of inhomogeneous fermionic matter in a wide range of different systems. The system is described using a Fermionic Shadow wavefunction (FSWF) and the energy is computed by means of the Variational Monte Carlo technique. The general form of FSWF is useful to describe many--body systems with the coexistence of different phases as well in the presence of defects or impurities, but it requires overcoming a significant sign problem. As an application, we studied the energy to activate vacancies in solid $^3$He.Comment: 4 pages, 2 figure