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

Research output: Contribution to journalArticlepeer-review

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

Original languageEnglish
JournalQuantum Machine Intelligence
Volume2
Issue number1
DOIs
Publication statusPublished - 2020 Jun 1

Keywords

  • Feature map
  • Kernel method
  • Quantum computing
  • Support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

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