IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i10p2525-d1655113.html
   My bibliography  Save this article

Quantum Neural Networks for Solving Power System Transient Simulation Problem

Author

Listed:
  • Mohammadreza Soltaninia

    (Department of Electrical Engineering, Alfred University, Alfred, NY 14802, USA
    These authors contributed equally to this work.)

  • Junpeng Zhan

    (Department of Electrical Engineering, Alfred University, Alfred, NY 14802, USA
    These authors contributed equally to this work.)

Abstract

Quantum computing, leveraging principles of quantum mechanics, represents a transformative approach in computational methodologies, offering significant enhancements over traditional classical systems. This study tackles the complex and computationally demanding task of simulating power system transients through solving differential-algebraic equations (DAEs). We introduce two novel Quantum Neural Networks (QNNs): the Sinusoidal-Friendly QNN and the Polynomial-Friendly QNN, proposing them as effective alternatives to conventional simulation techniques. Our application of these QNNs successfully simulates two small power systems, demonstrating their potential to achieve good accuracy. We further explore various configurations, including time intervals, training points, and the selection of classical optimizers, to optimize the solving of DAEs using QNNs. This research not only marks a pioneering effort in applying quantum computing to power system simulations but also expands the potential of quantum technologies in addressing intricate engineering challenges.

Suggested Citation

  • Mohammadreza Soltaninia & Junpeng Zhan, 2025. "Quantum Neural Networks for Solving Power System Transient Simulation Problem," Energies, MDPI, vol. 18(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2525-:d:1655113
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/10/2525/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/10/2525/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang Gao & Guojian Wu, 2023. "Application of Quantum Computing in Power Systems," Energies, MDPI, vol. 16(5), pages 1-3, February.
    2. Lars S. Madsen & Fabian Laudenbach & Mohsen Falamarzi. Askarani & Fabien Rortais & Trevor Vincent & Jacob F. F. Bulmer & Filippo M. Miatto & Leonhard Neuhaus & Lukas G. Helt & Matthew J. Collins & Adr, 2022. "Quantum computational advantage with a programmable photonic processor," Nature, Nature, vol. 606(7912), pages 75-81, June.
    3. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    4. J. M. Arrazola & V. Bergholm & K. Brádler & T. R. Bromley & M. J. Collins & I. Dhand & A. Fumagalli & T. Gerrits & A. Goussev & L. G. Helt & J. Hundal & T. Isacsson & R. B. Israel & J. Izaac & S. Jaha, 2021. "Quantum circuits with many photons on a programmable nanophotonic chip," Nature, Nature, vol. 591(7848), pages 54-60, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ojas Parekh, 2023. "Synergies Between Operations Research and Quantum Information Science," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 266-273, March.
    2. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    3. Sofia Priazhkina & Samuel Palmer & Pablo Martín-Ramiro & Román Orús & Samuel Mugel & Vladimir Skavysh, 2024. "Digital Payments in Firm Networks: Theory of Adoption and Quantum Algorithm," Staff Working Papers 24-17, Bank of Canada.
    4. X. L. He & Yong Lu & D. Q. Bao & Hang Xue & W. B. Jiang & Z. Wang & A. F. Roudsari & Per Delsing & J. S. Tsai & Z. R. Lin, 2023. "Fast generation of Schrödinger cat states using a Kerr-tunable superconducting resonator," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Xinbiao Wang & Yuxuan Du & Zhuozhuo Tu & Yong Luo & Xiao Yuan & Dacheng Tao, 2024. "Transition role of entangled data in quantum machine learning," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    6. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
    7. Martin Ringbauer & Marcel Hinsche & Thomas Feldker & Paul K. Faehrmann & Juani Bermejo-Vega & Claire L. Edmunds & Lukas Postler & Roman Stricker & Christian D. Marciniak & Michael Meth & Ivan Pogorelo, 2025. "Verifiable measurement-based quantum random sampling with trapped ions," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    8. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    9. Nikolaos Schetakis & Davit Aghamalyan & Michael Boguslavsky & Agnieszka Rees & Marc Rakotomalala & Paul Robert Griffin, 2024. "Quantum Machine Learning for Credit Scoring," Mathematics, MDPI, vol. 12(9), pages 1-12, May.
    10. Eric Vaz, 2024. "Quantum machine learning in spatial analysis: a paradigm shift in resource allocation and environmental modeling," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-13, December.
    11. Jin Ming Koh & Tommy Tai & Ching Hua Lee, 2024. "Realization of higher-order topological lattices on a quantum computer," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    12. Li, Nianqiao & Yan, Fei & Hirota, Kaoru, 2022. "Quantum data visualization: A quantum computing framework for enhancing visual analysis of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    13. Xu, Zhengmeng & Wang, Yujie & Feng, Xiaotong & Wang, Yilin & Li, Yanli & Lin, Hai, 2024. "Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions," Finance Research Letters, Elsevier, vol. 67(PA).
    14. Cambyse Rouzé & Daniel Stilck França & Emilio Onorati & James D. Watson, 2024. "Efficient learning of ground and thermal states within phases of matter," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    15. Elies Gil-Fuster & Jens Eisert & Carlos Bravo-Prieto, 2024. "Understanding quantum machine learning also requires rethinking generalization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    16. Guo, Mingchao & Liu, Hailing & Li, Yongmei & Li, Wenmin & Gao, Fei & Qin, Sujuan & Wen, Qiaoyan, 2022. "Quantum algorithms for anomaly detection using amplitude estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    17. Vicente Moret-Bonillo & Samuel Magaz-Romero & Eduardo Mosqueira-Rey, 2022. "Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    18. Gong, Li-Hua & Xiang, Ling-Zhi & Liu, Si-Hang & Zhou, Nan-Run, 2022. "Born machine model based on matrix product state quantum circuit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    19. Mark Dong & Julia M. Boyle & Kevin J. Palm & Matthew Zimmermann & Alex Witte & Andrew J. Leenheer & Daniel Dominguez & Gerald Gilbert & Matt Eichenfield & Dirk Englund, 2023. "Synchronous micromechanically resonant programmable photonic circuits," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    20. Laura Böhm & Sebastian Kolb & Thomas Plankenbühler & Jonas Miederer & Simon Markthaler & Jürgen Karl, 2023. "Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-25, September.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2525-:d:1655113. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.