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• W3103309536 abstract "Optimizing for a classification problem with a 0-1 loss function is an NP-hard problem [1] even for a simple binary classification task. As Preskill stated “We don't expect a quantum computer to solve worst case instances of NP-hard problems, but it might find better approximate solution or find it faster” [2]. Hybrid Quantum Classical Platforms harness the full capacity of Near-Term Quantum Computers. In this study, a focus is given on the variational circuits based approach accomplishing various machine learning tasks. We will survey a variety of experimental demonstrations conducted on actual quantum hardware and actively developing simulation software, anticipated to have a broad range of real-world applications in this increasingly growing field." @default.
• W3103309536 created "2020-11-23" @default.
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• W3103309536 date "2020-11-03" @default.
• W3103309536 modified "2023-09-28" @default.
• W3103309536 title "Survey on Hybrid Classical-Quantum Machine Learning Models" @default.
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• W3103309536 doi "https://doi.org/10.1109/ccci49893.2020.9256649" @default.
• W3103309536 hasPublicationYear "2020" @default.
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