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Distributed Quantum Machine Learning

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Innovations for Community Services (I4CS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1585))

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Abstract

Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms. In this paper we propose an approach for distributed quantum machine learning, which allows multiple parties to securely perform computations, without having to reveal their data. We will consider a distributed adder and a distributed distance-based classifier.

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Authors and Affiliations

  1. The Netherlands Organisation for Applied Scientific Research, Anna van Buerenplein 1, 2595DA, The Hague, The Netherlands

    Niels M. P. Neumann & Robert S. Wezeman

Authors
  1. Niels M. P. Neumann
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  2. Robert S. Wezeman
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Correspondence to Niels M. P. Neumann .

Editor information

Editors and Affiliations

  1. Netherlands Organisation for Applied Scientific Research, The Hague, The Netherlands

    Frank Phillipson

  2. Deutsche Telekom Technology & Innovation, Darmstadt, Germany

    Gerald Eichler

  3. University of Applied Sciences Jena, Jena, Germany

    Christian Erfurth

  4. University of Hagen, Hagen, Germany

    Günter Fahrnberger

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Neumann, N.M.P., Wezeman, R.S. (2022). Distributed Quantum Machine Learning. In: Phillipson, F., Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2022. Communications in Computer and Information Science, vol 1585. Springer, Cham. https://doi.org/10.1007/978-3-031-06668-9_20

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  • DOI: https://doi.org/10.1007/978-3-031-06668-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06667-2

  • Online ISBN: 978-3-031-06668-9

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