Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4125
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dc.contributor.authorRao, P.U.-
dc.contributor.authorSodhi, B.-
dc.date.accessioned2022-10-27T14:12:19Z-
dc.date.available2022-10-27T14:12:19Z-
dc.date.issued2022-10-27-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/4125-
dc.description.abstractBuilding a dependable network of electric vehicle charging stations (EVCSs) requires satisfying the demands and constraints of EV owners, energy grids, and the entities that will own and operate the EVCSs. Thus, determining the optimal spatial placement of EVCS becomes essential for the success of EVs in a market. Time taken by classical computers to solve such combinatorial optimization problems increases exponentially with the size of the area, making them non-scalable. We propose a novel quantum-classical solution to solve this problem. A crucial idea of our approach is to move the more complex combinatorial optimization portion of the problem into a quantum algorithm. We show that our solution gives more than 500% improvement in speed compared to the state-of-the-art classical methods, thus making it well suited for scalability scenarios.en_US
dc.language.isoen_USen_US
dc.subjectQuantum computingen_US
dc.subjectElectric vehicle charging station placementen_US
dc.subjectQuantum unconstrained binary optimizationen_US
dc.subjectVariational quantum circuitsen_US
dc.subjectQuantum machine learningen_US
dc.titleHybrid quantum-classical solution for electric vehicle charger placement problemen_US
dc.typeArticleen_US
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