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

doi:10.1016/j.tcs.2025.115649

2025-11-01

PRE

Hao Zhong

Qi Zhang *

DOI：10.1016/j.tcs.2025.115649

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摘要

En 中文

量子近似优化算法（QAOA，下同）在解决NP难组合优化问题上展现出巨大潜力。本研究提出了一种惩罚增强型QAOA框架，用于处理最大化与最小化问题。通过统一设置惩罚系数，该框架为两类问题提供了通用支持。它通过调整目标函数与哈密顿量构建，确保输出解的可行性并提升近似解的质量。我们将此框架应用于最小顶点覆盖问题（作为最小化任务）和最大独立集问题（作为最大化任务），并设计了相应的量子哈密顿量及惩罚项。

Keyword:

Quantum approximation optimization

algorithm

Hamiltonian

Vertex cover

Independent set

期刊

Theoretical Computer Science

IF:

论文数:

273

被引数:

1.0W

机构

guangzhou college of commerce

学者数:

论文数: 13

被引数: 0

guangdong polytechnic normal university

学者数:

658

论文数: 318

被引数: 0

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