  Population annealing is a Monte Carlo algorithm that marries features from
simulated annealing and parallel tempering Monte Carlo. As such, it is ideal to
overcome large energy barriers in the free-energy landscape while minimizing a
Hamiltonian. Thus, population annealing Monte Carlo can be used as a heuristic
to solve combinatorial optimization problems. We illustrate the capabilities of
population annealing Monte Carlo by computing ground states of the
three-dimensional Ising spin glass with Gaussian disorder, whilst comparing to
simulated annealing and parallel tempering Monte Carlo. Our results suggest
that population annealing Monte Carlo is significantly more efficient than
simulated annealing but comparable to parallel tempering Monte Carlo for
finding spin-glass ground states.
