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Communication Dans Un Congrès Année : 2023

Solving the Job Shop Scheduling Problem: QUBO model and Quantum Annealing

Résumé

Quantum optimization which is the use of quantum computers and algorithms for solving complex optimization problems, is one of the topics in quantum computing with the highest potential. Two types of approaches are generally used to solve combinatorial optimization problems with quantum computers: exact methods such as the Grover’s search algorithm, and meta-heuristics such as the Quantum Annealing and the Quantum Approximate Optimization Algorithm (QAOA). Exact methods and variational methods like QAOA make use of universal gate-based quantum computers, such as the ones developed by the IBM company.Quantum annealing is designed for analogic quantum computers developed by D-Wave. Solving a combinatorial optimization problem with heuristics generally requires a transformation of the problem to a format suitable for the quantum computer. While some existing frameworks can cope with Constrained Quadratic Models (CQM), Quadratic Unconstrained Binary Optimization (QUBO) is the best option to map an optimization problem to a quantum computer or simulator. We consider in this paper the job shop scheduling problem.
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Dates et versions

hal-04037312 , version 1 (30-03-2023)

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  • HAL Id : hal-04037312 , version 1

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Riad Aggoune, Samuel Deleplanque. Solving the Job Shop Scheduling Problem: QUBO model and Quantum Annealing. Emerging optimization methods: from metaheuristics to quantum approaches, Apr 2023, Troyes, France. ⟨hal-04037312⟩
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