Quantum annealing is a heuristic algorithm for solving combinatorial optimization and sampling problems. By introducing and modulating quantum fluctuations, quantum annealing attempts to find the minimum of a cost function; that is, the low-energy states that represent solutions to a problem.
Quantum annealing works by using the natural evolution of quantum systems. A fundamental rule of physics is that objects in a closed system tend to seek their minimum energy state. They slide down hills; they cool down over time. This behavior is also true in the world of quantum physics. This means that—if we can formulate a problem as an energy-minimization problem, whereby the its answer corresponds to its lowest energy state—we can use quantum physics to solve it.
Quantum annealing processors naturally return low-energy solutions; some applications require the real minimum energy and others require good low-energy samples. This approach is best suited to solving discrete optimization problems and probabilistic sampling problems.
For more information on quantum annealing, or to learn how the D-Wave system uses it to solve computational problems, watch the videos below and read the Getting Started guide on the D-Wave documentation site.