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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rao, P.U. | - |
dc.contributor.author | Sodhi, B. | - |
dc.date.accessioned | 2022-10-27T14:12:19Z | - |
dc.date.available | 2022-10-27T14:12:19Z | - |
dc.date.issued | 2022-10-27 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/4125 | - |
dc.description.abstract | Building 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.iso | en_US | en_US |
dc.subject | Quantum computing | en_US |
dc.subject | Electric vehicle charging station placement | en_US |
dc.subject | Quantum unconstrained binary optimization | en_US |
dc.subject | Variational quantum circuits | en_US |
dc.subject | Quantum machine learning | en_US |
dc.title | Hybrid quantum-classical solution for electric vehicle charger placement problem | en_US |
dc.type | Article | en_US |
Appears in Collections: | Year-2022 |
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Full Text.pdf | 1.67 MB | Adobe PDF | View/Open Request a copy |
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