Quantum Machine Learning for Secondary Frequency Control

Quantum Physics arXiv:2512.02065 (quant-ph) [Submitted on 29 Nov 2025] Title:Quantum Machine Learning for Secondary Frequency Control Authors:Younes Ghazagh Jahed, Alireza Khatiri View a PDF of the paper titled Quantum Machine Learning for Secondary Frequency Control, by Younes Ghazagh Jahed and 1 other authors View PDF Abstract:Frequency control in power systems is critical to maintaining stability and preventing blackouts. Traditional methods like meta-heuristic algorithms and machine learning face limitations in real-time applicability and scalability. This paper introduces a novel approach using a pure variational quantum circuit (VQC) for real-time secondary frequency control in diesel generators. Unlike hybrid classical-quantum models, the proposed VQC operates independently during execution, eliminating latency from classical-quantum data exchange. The VQC is trained via supervised learning to map historical frequency deviations to optimal Proportional-Integral (PI) controller parameters using a pre-computed lookup table. Simulations demonstrate that the VQC achieves high prediction accuracy (over 90%) with sufficient quantum measurement shots and generalizes well across diverse test events. The quantum-optimized PI parameters significantly improve transient response, reducing frequency fluctuations and settling time. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) ACM classes: I.2.8 Cite as: arXiv:2512.02065 [quant-ph] (or arXiv:2512.02065v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.02065 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Younes Ghazagh Jahed [view email] [v1] Sat, 29 Nov 2025 15:03:52 UTC (674 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Machine Learning for Secondary Frequency Control, by Younes Ghazagh Jahed and 1 other authorsView PDF view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cs cs.LG 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)