Dissipative learning of a quantum classifier

Ufuk Korkmaz*, Denz Türkpençe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The expectation that quantum computation might bring performance advantages in machine learning algorithms motivates the work on the quantum versions of artificial neural networks. In this study, we analyse the learning dynamics of a quantum classifier model that works as an open quantum system which is an alternative to the standard quantum circuit model. According to the obtained results, the model can be successfully trained with a gradient descent (GD)-based algorithm. The fact that these optimisation processes have been obtained with continuous dynamics, shows promise for the development of a differentiable activation function for the classifier model.

Original languageEnglish
Article number165
JournalPramana - Journal of Physics
Volume97
Issue number4
DOIs
Publication statusPublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, Indian Academy of Sciences.

Keywords

  • 03.65.Yz
  • 03.67.Ac
  • 03.67.Lx
  • 03.67.−a
  • Quantum learning
  • cost function
  • open quantum system
  • quantum classifier
  • training

Fingerprint

Dive into the research topics of 'Dissipative learning of a quantum classifier'. Together they form a unique fingerprint.

Cite this