SPLIT-Q：面向相干控制孤岛的可扩展序列量子计算框架
SPLIT-Q: A Scalable Sequential Quantum Computing Framework for Coherent Controlled Islanding
中文总结 AI 辅助
该研究针对经典控制孤岛方法计算成本随网络规模攀升的问题，提出量子比特受限的序列分布式QAOA框架，大幅降低量子资源需求，可在当前硬件下解决大型电力系统相干控制孤岛问题。
中文摘要 AI 辅助
分布式能源的日益整合提升了电力系统的变异性与不确定性，在扰动发生时，这些效应会加剧发负荷不平衡与级联故障。控制孤岛通过将受损电网划分为连通、电气可持续的孤岛来限制故障传播，但经典方法的计算成本随网络规模与孤岛数量的增长而快速攀升。量子优化为探索该组合分区空间提供了替代方案，然而整体式量子公式将所有分配决策编码至单个电路，导致量子比特需求与电路复杂度随网络规模缩放。本研究提出一种量子比特受限的序列分布式量子近似优化算法（QAOA）框架，以在有限量子资源下解决相干控制孤岛问题，其将优化问题表述为边界条件约束的区域二次无约束二元优化（QUBO）子问题，并在固定量子比特预算内依次求解，因此电路宽度与网络规模无关，在有界度网络上的总量子工作量呈线性缩放。评估采用11个9至300节点的IEEE系统，使用IBM量子计算资源，以Gurobi与整体式QAOA作为基准，在所有系统中，该框架在噪声环境下均恢复了Gurobi最优可行分区，验证了其解质量的鲁棒性；结果还表明，与整体式QAOA相比，所提方法大幅降低了量子资源需求与电路复杂度，使大型孤岛问题可在当前硬件限制内解决，该框架为大规模电力系统的量子优化提供了可行且可扩展的路径。
英文摘要
Growing integration of distributed energy resources increases power-system variability and uncertainty. During disturbances, these effects can intensify generation-load imbalances and cascading failures. Controlled islanding limits their propagation by partitioning a compromised grid into connected, electrically sustainable islands. However, classical methods face rapidly growing computational costs as network size and island count increase. Quantum optimization offers an alternative for exploring this combinatorial partition space. Yet monolithic quantum formulations encode all assignment decisions in one circuit, causing qubit demand and circuit complexity to scale with network size. In this study, a qubit-bounded sequential distributed quantum approximate optimization algorithm (QAOA) framework is proposed to tackle coherent controlled islanding under limited quantum resources. It formulates the optimization as boundary-conditioned regional quadratic unconstrained binary optimization (QUBO) subproblems that are solved sequentially within a fixed qubit budget. Thus, circuit width remains independent of network size, with aggregate quantum workload scaling linearly on bounded-degree networks. Evaluation covers eleven IEEE systems from 9 to 300 buses using IBM quantum computing resources, with Gurobi and monolithic QAOA as references. Across all systems, the framework recovers feasible Gurobi-optimal partitions under noise, confirming the resilience of its solution quality. The results further show that the proposed method substantially reduces quantum-resource demand and circuit complexity relative to monolithic QAOA, allowing large islanding problems to be addressed within current hardware limits. The proposed framework provides a feasible and scalable pathway for quantum optimization in large-scale power systems.
