Green Function Monte Carlo with Stochastic Reconfiguration: an effective remedy for the sign problem disease

Abstract

A recent technique, proposed to alleviate the ``sign problem disease'', is discussed in details. As well known the ground state of a given Hamiltonian $H$ can be obtained by applying the imaginary time propagator $e^{-H τ}$ to a given trial state $ψ_T$ for large imaginary time $τ$ and sampling statistically the propagated state $ ψ_τ = e^{-H τ} ψ_T$. However the so called ``sign problem'' may appear in the simulation and such statistical propagation would be practically impossible without employing some approximation such as the well known ``fixed node'' approximation (FN). This method allows to improve the FN dynamic with a systematic correction scheme. This is possible by the simple requirement that, after a short imaginary time propagation via the FN dynamic, a number $p$ of correlation functions can be further constrained to be {\em exact} by small perturbation of the FN propagated state, which is free of the sign problem. By iterating this scheme the Monte Carlo average sign, which is almost zero when there is sign problem, remains stable and finite even for large $τ$. The proposed algorithm is tested against the exact diagonalization results available on finite lattice. It is also shown in few test cases that the dependence of the results upon the few parameters entering the stochastic technique can be very easily controlled, unless for exceptional cases.