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- W3103938541 abstract "A method for analyzing the feature map for the kernel-based quantum classifier is developed; that is, we give a general formula for computing a lower bound of the exact training accuracy, which helps us to see whether the selected feature map is suitable for linearly separating the dataset. We show a proof of concept demonstration of this method for a class of 2-qubit classifier, with several 2-dimensional dataset. Also, a synthesis method, that combines different kernels to construct a better-performing feature map in a lager feature space, is presented." @default.
- W3103938541 created "2020-11-23" @default.
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- W3103938541 date "2020-06-01" @default.
- W3103938541 modified "2023-10-06" @default.
- W3103938541 title "Analysis and synthesis of feature map for kernel-based quantum classifier" @default.
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- W3103938541 doi "https://doi.org/10.1007/s42484-020-00020-y" @default.
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