Correct, although it is argued, that given "enough" time Simulated Annealing will have search the entire solution space and thus return the global optimum. Sim

Correct, although it is argued, that given "enough" time Simulated Annealing will have search the entire solution space and thus return the global optimum. Simulated Annealing is a great tool for optimization problems, because it is almost trivial to implement -- If you have a combinatorial problem, that you evaluate the objective function on (preferable evaluate changes easily), then SA is a good starting point. It is a meta-heuristic that can be applied to almost any problem. However, Simulated Annealing is almost never the best way. Better methods (more efficient/better results) are often either based on complex meta heuristics or custom heuristics for approximate results, or branch and bound or dynamic programming for guaranteed optimum. If quantum computing enables us to perform a massive speed-up with a simple SA-like meta-heuristic, then that would make "solving" NP-hard problems a much more common task. You could just insert your problem into this "black box", and a few seconds later you'll get the result.