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Quantum annealing
Quantum physics-based metaheuristic for optimization problems
Quantum annealing
Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete (combinatorial optimization problems) with many local minima, such as finding the ground state of a spin glass or solving QUBO problems, which can encode a wide range of problems like Max-Cut, graph coloring, SAT or the traveling salesman problem. The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as a quantum-inspired classical algorithm. It was formulated in its present form by T. Kadowaki and H. Nishimori (ja) in 1998, though an imaginary-time variant without quantum coherence had been discussed by A. B. Finnila, M. A. Gomez, C. Sebenik and J. D. Doll in 1994. Quantum annealing starts from a quantum-mechanical superposition of all possible states...
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