aiqbench analysis
This study presents a hybrid quantum-classical framework for distribution network reconfiguration, leveraging trapped-ion hardware to guide classical solvers toward near-optimal solutions with reduced computation time. By utilizing a novel cycle-edge encoding within the Quantum Alternating Operator Ansatz, the method ensures topological feasibility while adapting to limited qubit budgets across various grid sizes. The results demonstrate that quantum-derived sampling can significantly accelerate the search for low-loss network topologies in complex combinatorial optimization problems.
Two-Stage Quantum-Classical Distribution Network Reconfiguration via Cycle-Edge Encoding
arXiv:2609.40264v1 Announce Type: new Abstract: 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.
Abstract: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.
arXiv:2609.40264 [quant-ph]
(or arXiv:2609.40264v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.40264 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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