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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Vydáno v:Physics letters. A Ročník 553; s. 130690
Hlavní autoři: Li, Guang-Hui, Wang, Sha-Sha, Ni, Xiao-Hui, Gao, Fei, Guo, Fen-Zhuo, Qin, Su-Juan, Wen, Qiao-Yan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 05.09.2025
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ISSN:0375-9601
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Abstract 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.
AbstractList 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.
ArticleNumber 130690
Author Gao, Fei
Guo, Fen-Zhuo
Ni, Xiao-Hui
Wen, Qiao-Yan
Qin, Su-Juan
Li, Guang-Hui
Wang, Sha-Sha
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Keywords Parameterized quantum circuits
Quantum alternating operator ansatz
Minimum weighted vertex cover
Constrained combinatorial optimization problems
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Snippet The Quantum Alternating Operator Ansatz (QAOA+) extends the Quantum Approximate Optimization Algorithm (QAOA) to solve constrained combinatorial optimization...
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SubjectTerms Constrained combinatorial optimization problems
Minimum weighted vertex cover
Parameterized quantum circuits
Quantum alternating operator ansatz
Title Performance analysis of the quantum alternating operator ansatz for solving the minimum weighted vertex cover problem
URI https://dx.doi.org/10.1016/j.physleta.2025.130690
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