Performance analysis of the quantum alternating operator ansatz for solving the minimum weighted vertex cover problem

The Quantum Alternating Operator Ansatz (QAOA+) extends the Quantum Approximate Optimization Algorithm (QAOA) to solve constrained combinatorial optimization problems more effectively. In this study, we explore the application of QAOA+ to the Minimum Weighted Vertex Cover (MWVC) problem in graph the...

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Bibliographic Details
Published in:	Physics letters. A Vol. 553; p. 130690
Main Authors:	Li, Guang-Hui, Wang, Sha-Sha, Ni, Xiao-Hui, Gao, Fei, Guo, Fen-Zhuo, Qin, Su-Juan, Wen, Qiao-Yan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 05.09.2025
Subjects:	Constrained combinatorial optimization problems Minimum weighted vertex cover Parameterized quantum circuits Quantum alternating operator ansatz Parameterized quantum circuits Quantum alternating operator ansatz Minimum weighted vertex cover Constrained combinatorial optimization problems
ISSN:	0375-9601
Online Access:	Get full text
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Summary:	The Quantum Alternating Operator Ansatz (QAOA+) extends the Quantum Approximate Optimization Algorithm (QAOA) to solve constrained combinatorial optimization problems more effectively. In this study, we explore the application of QAOA+ to the Minimum Weighted Vertex Cover (MWVC) problem in graph theory and evaluate its performance through numerical experiments. The results indicate that although QAOA+ can solve the MWVC problem efficiently, the quality of its solution is lower compared to the unweighted Minimum Vertex Cover (MVC) problem on the same graph. The study reveals that vertex weights introduce dependencies between the parameters of different qubit gates in the quantum circuit, which restricts parameter optimization and brings additional complexity to the problem. Other algorithms generally encounter difficulty when moving from solving MVC problems to MWVC problems, with performance degrading on most weighted instances, while classical heuristic algorithms may perform better on weighted instances with the help of empirical rules or heuristic strategies.
ISSN:	0375-9601
DOI:	10.1016/j.physleta.2025.130690