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

Combinatorial optimization models a vast range of industrial processes aiming at improving their efficiency. In general, solving this type of problem exactly is computationally intractable. Therefore, practitioners rely on heuristic solution approaches. Variational quantum algorithms are optimizatio...

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Bibliographic Details
Published in:	EPJ quantum technology 2022-12, Vol.9 (1), Article 5
Main Authors:	Amaro, David, Rosenkranz, Matthias, Fitzpatrick, Nathan, Hirano, Koji, Fiorentini, Mattia
Format:	Article
Language:	eng
Subjects:	Algorithms Case studies Combinatorial analysis Hardware Job shop scheduling Job shops Nanotechnology and Microengineering Optimization Physics Physics and Astronomy Quantum Information Technology Quantum Physics Qubits (quantum computing) Special Issue on Quantum Industry Spintronics Steel making
Online Access:	Get full text
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Summary:	Combinatorial optimization models a vast range of industrial processes aiming at improving their efficiency. In general, solving this type of problem exactly is computationally intractable. Therefore, practitioners rely on heuristic solution approaches. Variational quantum algorithms are optimization heuristics that can be demonstrated with available quantum hardware. In this case study, we apply four variational quantum heuristics running on IBM’s superconducting quantum processors to the job shop scheduling problem. Our problem optimizes a steel manufacturing process. A comparison on 5 qubits shows that the recent filtering variational quantum eigensolver (F-VQE) converges faster and samples the global optimum more frequently than the quantum approximate optimization algorithm (QAOA), the standard variational quantum eigensolver (VQE), and variational quantum imaginary time evolution (VarQITE). Furthermore, F-VQE readily solves problem sizes of up to 23 qubits on hardware without error mitigation post processing.
ISSN:	2662-4400 2196-0763