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Communication Dans Un Congrès Année : 2024

Solving Edge-Weighted Maximum Clique Problem with DCA Warm-Start Quantum Approximate Optimization Algorithm

Résumé

The Quantum Approximate Optimization Algorithm is a hybrid quantum-classic algorithm used for solving combinatorial optimization. However, this algorithm performs poorly when solving the constrained combinatorial optimization problem. To deal with this issue, we consider the warm-start Quantum Approximate Optimization Algorithm for solving constrained problems. This article presents a new method for improving the performance of the Quantum Approximate Optimization Algorithm, with the Difference of Convex Optimization. Our approach focuses on the warm-start version of the algorithm and uses the Difference of Convex optimization to find the warm-start parameters. To show our method’s efficiency, we do several experiments on the edge-weighted maximum clique problem and see a good result.
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Dates et versions

hal-04636519 , version 1 (05-07-2024)

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Citer

Huy Phuc Nguyen Ha, Viet Hung Nguyen, Anh Son Ta. Solving Edge-Weighted Maximum Clique Problem with DCA Warm-Start Quantum Approximate Optimization Algorithm. Metaheuristics International Conference (MIC 2024), Jun 2024, Lorient, France. pp.246-261, ⟨10.1007/978-3-031-62912-9_24⟩. ⟨hal-04636519⟩
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