发表机构
UT-Battelle, LLC; Electric Power Board (EPB) of Chattanooga; Computational Science and Engineering Division, Oak Ridge National Laboratory; IonQ Inc.（UT-Battell有限责任公司; 田纳西州查塔努加电力委员会; 橡树岭国家实验室计算科学与工程部; IonQ公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出两阶段量子-经典配电网重构方法，利用环边编码和LR-QAOA搜索可行子空间，指导MISOCP求解器，在六个测试网络上达到最优解1%以内且耗时更短。
AI 中文摘要
配电网重构（DNR）是一个组合优化问题，旨在满足拓扑和电气运行约束的前提下，寻找低损耗的网络拓扑。在这项工作中，我们提出了一种用于DNR的两阶段量子-经典方法。首先，迭代线性斜坡量子交替算子拟设（LR-QAOA）搜索使用新颖的环边编码构建的拓扑可行子空间。该编码保证每个表示的配置都是生成树，同时允许根据可用量子比特预算调整每个子空间的大小和覆盖范围。其次，得到的样本分布用于指导混合整数二阶锥规划（MISOCP）求解器。该方法在六个配电网（从33节点到417节点）上通过仿真和在俘获离子量子硬件上执行进行了评估。在所有六个测试系统中，由硬件派生的分布指导的MISOCP求解器在比相应未指导求解器更短的中位求解时间内，达到了最佳已知解的1%以内的可行解。
英文摘要
Distribution network reconfiguration (DNR) is a combinatorial optimization problem that seeks a low-loss network topology subject to topological and electrical operating constraints. In this work, we propose a two-stage quantum-classical method for DNR. First, an iterative linear-ramp Quantum Alternating Operator Ansatz (LR-QAOA) searches topologically feasible subspaces constructed using a novel cycle-edge encoding. The encoding guarantees that every represented configuration is a spanning tree while allowing the size and coverage of each subspace to be adjusted to the available qubit budget. Second, the resulting sample distribution is used to guide a mixed-integer second-order-cone programming (MISOCP) solver. The method is evaluated on six distribution networks ranging from 33 to 417 buses through simulation and execution on trapped-ion quantum hardware. Across all six test systems, the MISOCP solver guided by hardware-derived distributions reaches an incumbent within 1\% of the best-known solution in less median solver time than the corresponding unguided solver.
Comments10 pages, 8 figures
