Green function monte carlo simulation of hamiltonian lattice models with stochastic reconfiguration

Matteo Beccaria A

David W. Heys

C. Stephen Hellberg, Efstratios Manousakis

Michael A. Lee, Kazi A. Motakabbir, Kevin E. Schmidt

Sandro Sorella

A new method for the stabilization of the sign problem in the Green function Monte Carlo technique is proposed. The method is devised for real lattice Hamiltonians and is based on an iterative ``stochastic reconfiguration'' scheme which introduces some bias but allows a stable simulation with constant sign. The systematic reduction of this bias is possible in principle. The method is applied to the frustrated ${J}_{1}{\ensuremath{-}J}_{2}$ Heisenberg model, and tested against exact diagonalization data. Evidence of a finite spin gap for ${J}_{2}{/J}_{1}>\ensuremath{\sim}0.4$ is found in the thermodynamic limit.

V. G. Rousseau

We present a stochastic Green function algorithm designed for bosons on lattices. This quantum Monte Carlo algorithm is independent of the dimension of the system, works in continuous imaginary time, and is exact (no error beyond statistical errors). Hamiltonians with several species of bosons (and one-dimensional Bose-Fermi Hamiltonians) can be easily simulated. Some important features of the algorithm are that it works in the canonical ensemble and gives access to n -body Green functions.