In both algorithms objective functions, that will be executed with non quantum computers, are used. Both algorithms are methods for finding the global minimum of a given objective function.
From wikipedia:
Quantum annealing can be compared to simulated annealing (SA), whose "temperature" parameter plays a similar role to QA's tunneling field strength.
What is meant by "tunneling field strength"?
The choice of next candidate state seems to be the only difference between the algorithms, is that correct?
Finally, what are the advantages of Quantum annealing? Is it faster, or is it just better at handling local minima?
Best Answer
The distance in which you randomly select the next candidate.
No. Normal simulated annealing works with fixed parameter, but quantum annealing always works with gradually decreasing parameter more like adaptive simulated annealing.
It can handle wider, but slightly different set of problems. Simulated annealing only works when the barriers separating minima are relatively low, but they can be any wide. The adaptive version compensates a bit, but it remains inefficient when the barriers are high.
Quantum annealing is not limited by barrier heights and because it starts with the parameter equal to diameter of the problem space it is not limited by their widths either. However it will have problems finding global minimum surrounded by large area of high values, because if it does not hit the small low area early, it won't get there after the parameter decreases.
So quantum annealing will usually give better results, but it might not depending on the specific problem.