量子增强多目标优化
Quantum-Enhanced Multi-Objective Optimization
- Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area（粤港澳大湾区量子科学中心）
- Department of Physics, Southern University of Science and Technology（南方科技大学物理系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究多目标组合优化难题，提出QEMOO框架，结合帕累托选择与热启动QAOA采样，引入自适应方向更新方案，在多轮协议下提升帕累托前沿超体积，为量子辅助多目标优化提供实用路径。
AI中文摘要：
多目标组合优化需要在相互冲突的目标中识别帕累托最优权衡解决方案，通常比单目标优化更具挑战性。尽管量子多目标优化方法已开始出现，但大多数现有量子优化工作流程仍围绕单目标或固定标量化设置构建。基于现有的加权和量子近似优化算法（QAOA）的量子多目标优化方法，我们提出了QEMOO，这是一个量子增强多目标优化框架，在相同的总测量次数预算下，通过多轮协议结合基于帕累托的选择和热启动QAOA采样。我们还引入了一种受PBI启发的自适应方向更新方案，以提高在强冲突基准区域中的覆盖率。在三个基准阶段，QEMOO在匹配测量次数预算下，比单通道加权和QAOA基线提高了帕累托前沿超体积，为高效测量量子辅助多目标优化及其未来应用提供了一条实用途径。
英文摘要：
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.
