Monte Carlo simulation of lattice boson systems

Hans A. De Raedt

Andrey G. Grozin, N.A. Grozina

Lode Pollet

This is a review of recent developments in Monte Carlo methods in the field of ultracold gases. For bosonic atoms in an optical lattice we discuss path-integral Monte Carlo simulations with worm updates and show the excellent agreement with cold atom experiments. We also review recent progress in simulating bosonic systems with long-range interactions, disordered bosons, mixtures of bosons and spinful bosonic systems. For repulsive fermionic systems, determinantal methods at half filling are sign free, but in general no sign-free method exists. We review the developments in diagrammatic Monte Carlo for the Fermi polaron problem and the Hubbard model, and show the connection with dynamical mean-field theory. We end the review with diffusion Monte Carlo for the Stoner problem in cold gases.

V. Apaja, Olav F. Syljuåsen

Allan S. Blaer, Jining Han

We present an algorithm for calculating the thermodynamic properties of a system of nonrelativistic bosons on a three-dimensional spatial lattice. The method, which maps the three-dimensional quantum system onto a four-dimensional classical system, uses Monte Carlo sampling of configurations in either the canonical or the grand canonical ensemble. Our procedure is applicable to any system of lattice bosons with arbitrary short-range interactions. We test the algorithm by computing the temperature dependence of the energy, the heat capacity, and the condensate fraction of the free Bose gas.

Ning Cai, Yuan 源 Gao 高, Wei Li +1 more

Abstract We extend a semiclassical numerical method, bosonic auxiliary-field Monte Carlo, to quantum spin systems. This method breaks the lattice into clusters, solves each cluster precisely and couples them with classical auxiliary fields through classical Monte Carlo simulation. We test the method with antiferromagnetic spin models in one-dimensional chains, square lattices and triangular lattices, and obtain reasonable results at finite temperatures. This algorithm builds a bridge between classical Monte Carlo method and quantum methods. The algorithm can be improved with either progress in classical Monte Carlo sampling or the development of quantum solvers, and can also be further applied to systems with different lattices or interactions.