Many of the most challenging optimization problems encountered in various disciplines of science and engineering, from biology and drug discovery to routing and scheduling can be reduced to NP-complete problems. Intuitively speaking, NP-complete problems are “hard to solve” because the number of operations that must be performed in order to find the solution grows exponentially with the problem size. The ubiquity of NP-complete problems has led to the development of dedicated hardware (such as optical annealing and quantum annealing machines like “D-Wave”) and special algorithms (heuristic algorithms like simulated annealing).
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Source: Phys.org