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arXiv · 1906.10467

Analysis and synthesis of feature map for kernel-based quantum classifier

Yudai Suzuki·Hiroshi Yano·Qi Gao·Shumpei Uno·Tomoki Tanaka·Manato Akiyama·Naoki Yamamoto
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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.

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Yudai Suzuki, Hiroshi Yano, Qi Gao, Shumpei Uno, Tomoki Tanaka, Manato Akiyama, Naoki Yamamoto. 2019-06-25. Analysis and synthesis of feature map for kernel-based quantum classifier. https://doi.org/10.1007/s42484-020-00020-y

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