Description: Kiwiboy
Quantum annealing vs. simulated annealing
Probabilistic search heuristics such as simulated annealing (SA) are very useful when the search space is extremely large or when the search landscape is non-convex such that basic hill climbing algorithms will tend to get stuck in a local minima.
Whilst SA works well for a variety of problems which are non-convex, quantum annealing can prove to be better for problems where the search landscape is extremely jagged with high variance of values. As SA depends on the temperature of the system to determine transition probabilities to a worse solution, this can be problematic for search spaces where there may be extreme variance in energy between neighboring inputs.