Efficient quantum correlated optimization of industrial planning

Optimization of NP-hard problems is a promising application of near-term quantum computers. Unfortunately, in spite of the huge efforts invested in the field, so far, quantum computers have not been able to demonstrate a commercial value in the solution of optimization problems. We attribute this failure to the lack of suitable models of real-life problems that can efficiently be run on quantum computers. We state 4 conditions that are needed to satisfy this requirement and identify a model of industrial planning that satisfies them. Even for this model, available quantum algorithms are outperformed by classical methods such as greedy approaches and linear programming. We show that Quantymize's proprietary approach, ``quantum correlated optimization'', is capable of obtaining the best solution in a broad range of parameters. We benchmark this approach both on a quantum simulator and on a Dwave quantum annealer, demonstrating a favorable scaling as a function of the number of variables. Our work paves the way for the use of quantum computers to solve real-life, large-scale problems.