A case study of variational quantum algorithms for a job shop scheduling problem

In this unique case study, we compare four variational quantum heuristics that can optimize a manufacturing process used by Japan’s Nippon Steel Corporation.
Results from running those algorithms on IBM quantum hardware show that CQ’s recent filtering variational quantum eigensolver (F-VQE) converges faster and samples the global optimum more frequently than the other algorithms, including the quantum approximate optimization algorithm (QAOA). In our experiments, F-VQE readily solved problem sizes using up to 23 qubits thus confirming that F-VQE can accelerate the pace at which current quantum hardware can solve optimization problems of industrial scale.
David Amaro, Matthias Rosenkranz, Nathan Fitzpatrick, Koji Hirano, Mattia Fiorentini
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