PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding

Quantum Physics arXiv:2608.02789 (quant-ph) [Submitted on 3 Aug 2026] Title:PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding Authors:Yuqi Jiang, Zhiding Liang, Qiang Guan, Yan Li, Ganesh Kumar Venayagamoorthy View a PDF of the paper titled PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding, by Yuqi Jiang and 4 other authors View PDF HTML (experimental) Abstract:Controlled islanding partitions a stressed power network to limit disrupted power transfer while preserving operational integrity in every island. This NP-hard partitioning problem becomes increasingly demanding as networks grow, motivating quantum optimization as a complementary approach. However, limited qubit capacity restricts the scale at which conventional QAOA can address islanding. This paper develops a qubit-efficient hybrid quantum formulation that overcomes this barrier. A physics-informed compact encoding captures essential islanding decisions while exploiting grid structure, with formal guarantees preserving the feasible solution space and optimization objective. A qubit-efficient Lagrangian strategy combines quantum optimization with classical refinement to enforce operational constraints. Complexity analysis shows that for fixed island counts on sparse graphs, the formulation reduces phase-separator and per-layer gate complexity from quadratic to linear scaling. Evaluations on eight IEEE systems (9 to 89 buses) across multiple quantum backends produce feasible, high-quality solutions under practical circuit and sampling budgets. Factorial ablation attributes resource and runtime gains to the complementary effects of compact encoding and qubit-efficient Lagrangian constraint handling. Noise analysis demonstrates stable solution quality under device noise, and landscape diagnostics reveal smoother, more consistently scaled QAOA cost surfaces. These results provide a transferable pathway for scaling constrained quantum optimization toward larger real-world applications on near-term hardware. Subjects: Quantum Physics (quant-ph); Systems and Control (eess.SY) Cite as: arXiv:2608.02789 [quant-ph] (or arXiv:2608.02789v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2608.02789 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuqi Jiang [view email] [v1] Mon, 3 Aug 2026 18:42:00 UTC (10,764 KB) Full-text links: Access Paper: View a PDF of the paper titled PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding, by Yuqi Jiang and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-08 Change to browse by: cs cs.SY eess eess.SY References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)