Quantum-Enhanced Multi-Objective Optimization
Abstract
Multi-objective combinatorial optimization requires identifying Pareto-optimal trade-off solutions among conflicting objectives, often making it more demanding than its single-objective counterpart. Although quantum multi-objective optimization methods have begun to emerge, most existing quantum optimization workflows are still built around single-objective or fixed-scalarization settings. Building on existing weighted-sum QAOA approaches to quantum multi-objective optimization, we propose QEMOO, a quantum-enhanced multi-objective optimization framework that combines Pareto-based selection and warm-started QAOA sampling in a multi-round protocol under the same total shot budget. We further introduce a PBI-inspired adaptive direction-update scheme to improve coverage in strongly conflicting benchmark regimes. Across three benchmark stages, QEMOO improves Pareto-front hypervolume over the single-pass weighted-sum QAOA baseline under matched shot budgets, suggesting a practical route toward shot-efficient quantum-assisted multi-objective optimization and its future applications.
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BibTeX
@misc{luo2026quantumenhanced,
title = {{Quantum-Enhanced Multi-Objective Optimization}},
author = {Maolin Luo and Jiapei Zhuang and Zuoheng Zou and Man-Hong Yung},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.18848},
eprint = {2607.18848},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.18848},
}
RIS
TY - UNPB
TI - Quantum-Enhanced Multi-Objective Optimization
AU - Maolin Luo
AU - Jiapei Zhuang
AU - Zuoheng Zou
AU - Man-Hong Yung
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2607.18848
UR - https://arxiv.org/abs/2607.18848
AB - Multi-objective combinatorial optimization requires identifying Pareto-optimal trade-off solutions among conflicting objectives, often making it more demanding than its single-objective counterpart. Although quantum multi-objective optimization methods have begun to emerge, most existing quantum optimization workflows are still built around single-objective or fixed-scalarization settings. Building on existing weighted-sum QAOA approaches to quantum multi-objective optimization, we propose QEMOO, a quantum-enhanced multi-objective optimization framework that combines Pareto-based selection and warm-started QAOA sampling in a multi-round protocol under the same total shot budget. We further introduce a PBI-inspired adaptive direction-update scheme to improve coverage in strongly conflicting benchmark regimes. Across three benchmark stages, QEMOO improves Pareto-front hypervolume over the single-pass weighted-sum QAOA baseline under matched shot budgets, suggesting a practical route toward shot-efficient quantum-assisted multi-objective optimization and its future applications.
ER -