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

被引：0
|
作者：
Huy Phuc Nguyen Ha [1 ]
Viet Hung Nguyen [2 ]
Anh Son Ta [1 ]
机构：
[1] Hanoi Univ Sci & Technol, Sch Appl Math & Informat, Hanoi, Dai Co Viet, Vietnam
[2] Univ Clermont Auvergne, LIMOS, Clermont Auvergne INP, CNRS,Mines St Etienne, Clermont Ferrand, France
来源：
METAHEURISTICS, MIC 2024, PT I | 2024年 / 14753卷
关键词：
Maximum edge-weighted clique; QAOA; warm-start; DCA;
D O I：
10.1007/978-3-031-62912-9_24
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：246 / 261
页数：16
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