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

研究成果: Article査読

22 被引用数 (Scopus)


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 datasets. Also, a synthesis method, which combines different kernels to construct a better-performing feature map in a lager feature space, is presented.

ジャーナルQuantum Machine Intelligence
出版ステータスPublished - 2020 6月 1

ASJC Scopus subject areas

  • 人工知能
  • 計算理論と計算数学
  • ソフトウェア
  • 応用数学
  • 理論的コンピュータサイエンス


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