Stochastic series expansion method for quantum Ising models with arbitrary interactions

A quantum Monte Carlo algorithm for the transverse Ising model with arbitrary short- or long-range interactions is presented. The algorithm is based on sampling the diagonal matrix elements of the power-series expansion of the density matrix (stochastic series expansion), and avoids the interaction summations necessary in conventional methods. In the case of long-range interactions, the scaling of the computation time with the system size N is therefore reduced from N2 to N ln(N). The method is tested on a one-dimensional ferromagnet in a transverse field, with interactions decaying as 1/r(2).