Hybrid quantum-classical neural networks for real-time fault detection in power systems.
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| Title: | Hybrid quantum-classical neural networks for real-time fault detection in power systems. |
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| Authors: | Hashmi H; Department of Computer Science & Engineering, Moradabad Institute of Technology, Moradabad India., Kumar A; Department of CSE, SET, Manav Rachna International Institute of Research and Studies (Deemed to be University), Faridabad, India., Kuttan SR; Department of Computer Science and Engineering, Chouksey Engineering College, Bilaspur, Chhattisgarh, India., Moolchandani J; Department of Computer Science & Engineering, Amity School of Engineering and Technology, Amity University, Gwalior, Madhaya Pradesh, India., Goel P; Department of Artificial Intelligence and Machine Learning, Manipal University Jaipur, Jaipur, Rajasthan, India., Smerat A; Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, Jordan.; Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India., Massebo YA; Department of English Language and Literature, Gambella University, Gambela, Ethiopia., Hashmi A; Department of Information Systems, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia. |
| Source: | PloS one [PLoS One] 2026 Jun 18; Vol. 21 (6), pp. e0349887. Date of Electronic Publication: 2026 Jun 18 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0349887 |
