  I propose a "quantum annealing" heuristic for the problem of combinatorial
search among a frustrated set of states characterized by a cost function to be
minimized. The algorithm is probabilistic, with postselection of the
measurement result. A unique parameter playing the role of an effective
temperature governs the computational load and the overall quality of the
optimization. Any level of accuracy can be reached with a computational load
independent of the dimension {\it N} of the search set by choosing the
effective temperature correspondingly low. This is much better than classical
search heuristics, which typically involve computation times growing as powers
of log({\it N})
