Penalty-enhanced quantum approximate optimization algorithm framework for maximization and minimization problems

The Quantum Approximate Optimization Algorithm (QAOA for short) has demonstrated great potential in solving NP-hard combinatorial optimization problems. This study proposes a penalty-enhanced QAOA framework for addressing both maximization and minimization problems. By uniformly setting penalty coef...

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Vydáno v:Theoretical computer science Ročník 1061; s. 115649
Hlavní autoři: Zhong, Hao, Zhang, Qi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 19.01.2026
Témata:
Hamiltonian
Independent set
Quantum approximation optimization algorithm
Vertex cover
Quantum approximation optimization algorithm
Vertex cover
Independent set
Hamiltonian
ISSN:0304-3975
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Shrnutí:The Quantum Approximate Optimization Algorithm (QAOA for short) has demonstrated great potential in solving NP-hard combinatorial optimization problems. This study proposes a penalty-enhanced QAOA framework for addressing both maximization and minimization problems. By uniformly setting penalty coefficients, the framework provides general support for both types of problems. It ensures the feasibility of output solutions and improves the quality of approximate solutions by adjusting the objective function and the construction of the Hamiltonian. We apply this framework to the Minimum Vertex Cover problem (as a minimization task) and the Maximum Independent Set problem (as a maximization task), designing corresponding quantum Hamiltonians and penalty terms.
ISSN:0304-3975
DOI:10.1016/j.tcs.2025.115649