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

页码：246 / 261

页数：16

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