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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2022: Advances in Knowledge Discovery and Data Mining pp 96–108Cite as

Effect of Different Encodings and Distance Functions on Quantum Instance-Based Classifiers

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13281))

Abstract

In the last years, we have witnessed the increasing usage of machine learning technologies. In parallel, we have observed the raise of quantum computing, a paradigm for computing making use of quantum theory. Quantum computing can empower machine learning with theoretical properties allowing to overcome the limitations of classical computing. The translation of classical algorithms into their quantum counter-part is not trivial and hides many difficulties. We illustrate and implement alternatives for the quantum nearest neighbor classifier focusing on the challenges related to data preparation and their effect on the performance. We show that, with certain data preparation strategies, quantum algorithms are comparable with the classic version, yet allowing for a theoretical reduction of the complexity for distances calculation.

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Notes

  1. 1.

    Python code available at: https://github.com/Brotherhood94/quantum_knn. We implemented QKNN using the qiskit library: https://qiskit.org/.

  2. 2.

    https://scikit-learn.org/stable. For mnist we focus on the task of classification between “0” and “8”.

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Acknowledgment

This work is supported by Università di Pisa under the “PRA - Progetti di Ricerca di Ateneo” (Institutional Research Grants) - Pr. no. PRA_2020-2021_92, “Quantum Computing, Technologies and Applications”, and the work of Gianna Del Corso was supported also by GNCS-INdAM.

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Authors and Affiliations

  1. University of Pisa, Pisa, Italy

    Alessandro Berti, Anna Bernasconi, Gianna M. Del Corso & Riccardo Guidotti

Authors
  1. Alessandro Berti
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  2. Anna Bernasconi
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  3. Gianna M. Del Corso
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  4. Riccardo Guidotti
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Corresponding author

Correspondence to Alessandro Berti .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yang Yu

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  5. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

    Yu Zheng

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Fei Teng

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Berti, A., Bernasconi, A., Del Corso, G.M., Guidotti, R. (2022). Effect of Different Encodings and Distance Functions on Quantum Instance-Based Classifiers. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13281. Springer, Cham. https://doi.org/10.1007/978-3-031-05936-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-05936-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05935-3

  • Online ISBN: 978-3-031-05936-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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