The theory of the quantum kernel-based binary classifier

Daniel K. Park, Carsten Blank, Francesco Petruccione

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


Binary classification is a fundamental problem in machine learning. Recent development of quantum similarity-based binary classifiers and kernel method that exploit quantum interference and feature quantum Hilbert space opened up tremendous opportunities for quantum-enhanced machine learning. To lay the fundamental ground for its further advancement, this work extends the general theory of quantum kernel-based classifiers. Existing quantum kernel-based classifiers are compared and the connection among them is analyzed. Focusing on the squared overlap between quantum states as a similarity measure, the essential and minimal ingredients for the quantum binary classification are examined. The classifier is also extended concerning various aspects, such as data type, measurement, and ensemble learning. The validity of the Hilbert-Schmidt inner product, which becomes the squared overlap for pure states, as a positive definite and symmetric kernel is explicitly shown, thereby connecting the quantum binary classifier and kernel methods.

Original languageEnglish
Article number126422
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Issue number21
Publication statusPublished - 2020 Jul 27

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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