发表机构
Pennsylvania State University; Rensselaer Polytechnic Institute; Kent State University; Clemson University; University of Pretoria（宾夕法尼亚州立大学; 伦斯勒理工学院; 肯特州立大学; 克莱姆森大学; 比勒陀利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PACE-QAOA方法，通过物理启发的紧凑编码与高效量子比特拉格朗日策略，将QAOA的门复杂度从二次降为线性，在8个IEEE电力系统上验证了可行高质量解，为约束量子优化扩展提供了途径。
AI 中文摘要
受控孤岛划分是将受扰动的电力网络进行分区，以限制电力传输中断，同时保持每个孤岛的运行完整性。这一NP难题随着网络规模扩大而愈发复杂，促使量子优化成为一种互补方法。然而，有限的量子比特容量限制了传统QAOA解决孤岛划分问题的规模。本文提出了一种高效量子比特的混合量子公式，以克服这一障碍。一种物理启发的紧凑编码在捕捉孤岛划分关键决策的同时，利用了电网结构，并有形式化保证保留可行解空间和优化目标。一种高效量子比特的拉格朗日策略将量子优化与经典优化相结合，以强制满足运行约束。复杂性分析表明，对于稀疏图上固定的孤岛数量，该公式将相分离器和每一层的门复杂度从二次缩放降低为线性缩放。在8个IEEE系统（9至89个母线）上，在多个量子后端进行的评估在实际电路和采样预算下产生了可行的高质量解决方案。因子消融分析将资源和运行时间的增益归因于紧凑编码与高效量子比特拉格朗日约束处理的互补效应。噪声分析表明，在设备噪声下解决方案质量稳定，而景观诊断显示QAOA代价表面更平滑、缩放更一致。这些结果为将约束量子优化扩展到近期硬件上更大的实际应用提供了可迁移的途径。
英文摘要
Increasing renewable-energy penetration heightens power-system variability and complicates disturbance containment. Controlled islanding mitigates cascading failures by partitioning a stressed network to limit disrupted power transfer while preserving each island's operational integrity, but this constrained partitioning problem is NP-hard. Although QAOA offers a complementary search strategy, limited near-term qubit capacity restricts conventional formulations. This paper presents a qubit-efficient hybrid quantum framework combining a physics-informed compact encoding with Lagrangian constraint handling and classical feasibility refinement. The encoding exploits grid structure while formally preserving the original feasible solution space and objective. For a fixed island count on sparse working graphs, the formulation reduces phase-separator and per-layer gate complexity from quadratic to linear scaling with system size. Tests on eight IEEE systems ranging from 9 to 89 buses and multiple quantum-provider backends produce feasible, high-quality islanding solutions under practical circuit and sampling budgets. Factorial ablation attributes resource and runtime improvements to the complementary effects of compact encoding and qubit-efficient constraint handling. Noise analysis shows stable solution quality under tested device noise, while landscape diagnostics reveal smoother, more consistently scaled QAOA cost surfaces and improved parameter-optimization behavior. These results offer a transferable approach for scaling constrained quantum optimization toward larger real-world applications on near-term hardware.
