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

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...

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Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Suzuki, Yudai, Yano, Hiroshi, Gao, Qi, Uno, Shumpei, Tanaka, Tomoki, Akiyama, Manato, Yamamoto, Naoki
Format: Article
Language:English
Subjects:
Classifiers
Datasets
Decomposition
Feature maps
Lower bounds
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1906.10467