基于量子计算的互联电网求解器
Quantum computing-based solver for interacting power grids
AI总结：
针对大规模互联电网关键模式评估的经典计算瓶颈，本文提出量子-混合方法，采用RVVQE算法处理非厄米导纳矩阵，经5节点系统验证，可规避经典内存限制，实现洲际级电网共振不稳定性诊断。
AI中文摘要：
多端输电电网中电力电子设备的普及日益引发谐波畸变与动态不稳定性。共振模式分析（RMA）虽能深入洞察系统共振，但评估大规模电网的关键模式存在严重计算瓶颈：经典迭代技术需在宽频范围内持续对角化超高维非厄米导纳矩阵，该过程会迅速耗尽经典内存与处理能力。为突破这一规模限制，本文提出一种新型量子-经典混合方法，将输电电网的导纳矩阵原生映射至量子处理单元（QPU）。由于电网矩阵为非厄米矩阵，标准量子特征求解器无法满足需求，故采用基于实方差的变分量子特征求解器（RVVQE）算法精准提取代表系统模式的复特征值。经标准5节点输电系统验证，量子衍生的关键共振模态阻抗与精确经典频率响应近乎完全匹配。至关重要的是，该方法通过将电网状态对数编码至量子内存，规避了经典RAM的限制。RVVQE框架的成功实现不仅搭建了耗散电网与开放量子系统的数学拓扑桥梁，还提供了具备强可扩展性的架构，可用于诊断当前超出经典计算边界的大型洲际级网络的共振不稳定性。
英文摘要：
The proliferation of power electronics in multi-terminal transmission grids has increasingly led to harmonic distortions and dynamic instabilities. While Resonance Mode Analysis (RMA) provides deep insights into these system resonances, evaluating the critical modes of large-scale grids presents a severe computational bottleneck. Classical iterative techniques must continuously diagonalize massively high-dimensional, non-Hermitian admittance matrices across a wide frequency spectrum, a process that rapidly exhausts classical memory and processing limits. To overcome this scaling barrier, we propose a novel quantum-classical hybrid methodology that natively maps the transmission grid's admittance matrix onto a Quantum Processing Unit (QPU). Because the grid's matrix is non-Hermitian, standard quantum eigensolvers are insufficient; thus, we employ the Real Variance-based Variational Quantum Eigensolver (RVVQE) algorithm to accurately extract the complex eigenvalues that represent the system's modes. Validated against a standard 5-bus transmission system, the quantum-derived critical-resonance modal impedances demonstrate near-perfect alignment with the exact classical frequency responses. Crucially, by encoding the grid's state logarithmically into quantum memory, this methodology bypasses classical RAM limitations. The successful implementation of the RVVQE framework not only bridges the mathematical topologies of dissipative electrical grids and open quantum systems but also provides a profoundly scalable architecture capable of diagnosing resonance instabilities in massive, continental-scale networks that currently exceed classical computational boundaries.
