Training an open quantum classifier

Ufuk Korkmaz, Melih Can Topal, Ekin Aygul, Deniz Turkpence

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Quantum machine learning (QML) aims to embed the power of quantum computation with learning theory. Quan-Tum noise and finding the best recipe for encoding classical information into a quantum register could be seen as challenges to overcome for computational performance. Classification of quantum information is a subtask for QML. In this study, we adopt a dissipative route for quantum data classification and examine the developed theory on a gradient descent-based learning task. In particular, we follow repeated interactions based on open quantum dynamics where the binary decision is encoded on a steady state. Based on the analytical results, we develop a cost function for training an open quantum neuron. We demonstrate that the dissipation-driven protocol is suitable for a supervised learning scheme.

Original languageEnglish
Title of host publicationISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages429-433
Number of pages5
ISBN (Electronic)9781665470131
DOIs
Publication statusPublished - 2022
Event6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 - Ankara, Turkey
Duration: 20 Oct 202222 Oct 2022

Publication series

NameISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings

Conference

Conference6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022
Country/TerritoryTurkey
CityAnkara
Period20/10/2222/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • cost function
  • open quantum system
  • quantum classifier
  • quantum learning
  • training

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