Variational Monte Carlo Metropolis sampling

The strategy in Variational Monte Carlo (VMC) is therefore to pick a proper form for a trial wave function based on physical insight for the particular system under study. In general, a number of parameters (oi,..., a ) will appear in the wave function to be treated as variational parameters. For any given set of a the Metropolis algorithm is used to sample the distribution... 

In variational Monte Carlo (VMC), one samples, using the Metropolis rejection method, the square of an assumed trial wavefunction, j where I = r, are the coordinates of all the particles... 

The variational quantum Monte Carlo method (VMC) is both simpler and more efficient than the DMC method, but also usually less accurate. In this method the Rayleigh-Ritz quotient for a trial function 0 is evaluated with Monte Carlo integration. The Metropolis-Hastings algorithm " is used to sample the distribution... 

In the variational quantum Monte Carlo (VQMC) method, the expectation value of the energy ( ) and/or another average property of a system is determined by Monte Carlo integrations. The expectation value of the energy is typically determined for a trial function j/q using Metropolis sampling based on tj/g. It is given by... 

This review article is divided into two major sections, the first of which details the theoretical basis of RQMC (Sect. 18.2). Initially we describe quantum Monte Carlo sampling from the pure distribution and mixed distribution F, showing that the RQMC approach to sample from the pure distribution rests on Metropolis-Hastings (MH [25,26]) sampling, as does the variational path integral (VPI [27]) method. As already mentioned, RQMC proposes reptafion-type moves while... 

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