Quantum computing: this is how quantum annealers solve complicated optimization tasks

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Quantum annealing systems promise to cope with optimization tasks more efficiently than classic computers ever could. It is seldom a theoretical question, but mainly a practical one: “Which is the shortest route?” “Which transmission quality is optimal?” “How do we get maximum profit?” With questions like this one tries to determine the best possible values ​​for one or more variables.

In the case of two variables, optimization tasks can be imagined as a hilly landscape: Longitude and latitude correspond to the two variables, so each location stands for a certain combination of values. The height of the landscape at a location stands for the quality of the solution, if one uses this combination of values. It is often defined that better values ​​are smaller. For the hilly landscape it follows that good solutions can be found in the valleys and bad ones on the peaks.

Focus on quantum computing

Real use cases, however, often have hundreds of parameters. For example, one does not try to find the best route for a single car, but rather wants to minimize the total time in a traffic system in which hundreds or thousands of vehicles are traveling. Landscapes with hundreds of dimensions cannot be imagined spatially, but the principle remains the same: every point on the landscape has a value c. The smaller c, the better the parameters at the point.

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