  We analyze the performance of quantum annealing as a heuristic optimization
method to find the absolute minimum of various continuous models, including
landscapes with only two wells and also models with many competing minima and
with disorder. The simulations performed using a projective quantum Monte Carlo
(QMC) algorithm are compared with those based on the finite-temperature
path-integral QMC technique and with classical annealing. We show that the
projective QMC algorithm is more efficient than the finite-temperature QMC
technique, and that both are inferior to classical annealing if this is
performed with appropriate long-range moves. However, as the difficulty of the
optimization problem increases, classical annealing looses efficiency, while
the projective QMC algorithm keeps stable performance and is finally the most
effective optimization tool. We discuss the implications of our results for the
outstanding problem of testing the efficiency of adiabatic quantum computers
using stochastic simulations performed on classical computers.
