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A Quantum-inspired Version of the Classification Problem

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Abstract

We address the problem of binary classification by using a quantum version of the Nearest Mean Classifier (NMC). Our proposal is indeed an advanced version of previous one (see Sergioli et al. 2017 that i) is able to be naturally generalized to arbitrary number of features and ii) exhibits better performances with respect to the classical NMC for several datasets. Further, we show that the quantum version of NMC is not invariant under rescaling. This allows us to introduce a free parameter, i.e. the rescaling factor, that could be useful to get a further improvement of the classification performance.

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Notes

  1. For the sake of the clarity regarding the indexes, we accord to use superscript index to indicate the different components of the vector and subscript to indicate different vectors.

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Acknowledgements

This work is supported by the Sardinia Region Project ”Modeling the uncertainty: quantum theory and imaging processing”, LR 7/8/2007. RAS CRP-59872 and by the Firb Project ”Structures and Dynamics of Knowledge and Cognition” [F21J12000140001]. GMB acknowledges support from CONICET and UNLP (Argentina).

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Authors and Affiliations

  1. University of Cagliari, Via Is Mirrionis 1, I-09123, Cagliari, Italy

    Giuseppe Sergioli, Enrica Santucci & Roberto Giuntini

  2. Instituto de Física La Plata, UNLP, CONICET, Facultad de Ciencias Exactas, 1900, La Plata, Argentina

    Gustavo Martin Bosyk

Authors
  1. Giuseppe Sergioli
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  2. Gustavo Martin Bosyk
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  3. Enrica Santucci
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  4. Roberto Giuntini
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Corresponding author

Correspondence to Giuseppe Sergioli.

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Sergioli, G., Bosyk, G.M., Santucci, E. et al. A Quantum-inspired Version of the Classification Problem. Int J Theor Phys 56, 3880–3888 (2017). https://doi.org/10.1007/s10773-017-3371-1

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  • DOI: https://doi.org/10.1007/s10773-017-3371-1

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