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Experimental kernel-based quantum machine learning in finite feature space
2020
Scientific Reports
We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer. Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space. In order to maximize the application range of the
doi:10.1038/s41598-020-68911-5
pmid:32704032
fatcat:2djf7cijz5fkpjngkgocqtirhy