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Quantum Variational Multi-class Classifier for the Iris Data Set

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Computational Science – ICCS 2022 (ICCS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13353))

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Abstract

Recent advances in machine learning on quantum computers have been made possible mainly by two discoveries. Mapping the features into exponentially large Hilbert spaces makes them linearly separable—quantum circuits perform linear operations only. The parameter-shift rule allows for easy computation of objective function gradients on quantum hardware—a classical optimizer can then be used to find its minimum. This allows us to build a binary variational quantum classifier that shows some advantages over the classical one. In this paper we extend this idea to building a multi-class classifier and apply it to real data. A systematic study involving several feature maps and classical optimizers as well as different repetitions of the parametrized circuits is presented. The accuracy of the model is compared both on a simulated environment and on a real IBM quantum computer.

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Notes

  1. 1.

    https://research.ibm.com/blog/127-qubit-quantum-processor-eagle.

  2. 2.

    https://quantum-computing.ibm.com/services?services=systems.

  3. 3.

    https://github.com/odisei369/quantum_learning_iris.

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Authors and Affiliations

  1. Institute of Information Technology, Warsaw University of Life Sciences—SGGW, ul. Nowoursynowska 159, 02-776, Warsaw, Poland

    Ilya Piatrenka & Marian Rusek

Authors
  1. Ilya Piatrenka
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  2. Marian Rusek
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Corresponding author

Correspondence to Marian Rusek .

Editor information

Editors and Affiliations

  1. Brunel University London, London, UK

    Derek Groen

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Piatrenka, I., Rusek, M. (2022). Quantum Variational Multi-class Classifier for the Iris Data Set. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_21

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  • DOI: https://doi.org/10.1007/978-3-031-08760-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08759-2

  • Online ISBN: 978-3-031-08760-8

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