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Pattern Recognition with Quantum Neural Networks

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Advances in Pattern Recognition — ICAPR 2001 (ICAPR 2001)

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Abstract

New model of quantum neural nework able to solve classification problems is presented. It is based on the extention of the model of quantum associative memory [1] and also utilizes Everett’s interpretation of quantum mechanics [2]–[4]. For presented model not neural weights but quantum entanglement is responsible for associations between input and output patterns. Distributed form of queries permits the system to generalize. Spurious memory trick is used to control the number of Grover’s iterations which is necessary to transform initial quantum state into the state which can give correct classification in most measurements. Numerical modelling of counting problem illustrates model’s behavior and its potential benefits.

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Author information

Authors and Affiliations

  1. Troitsk Institute of Innovation and Fusion Researches, Troitsk, Moscow region, Russia

    A.A. Ezhov

Authors
  1. A.A. Ezhov
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, EX4 4PT, Exeter, UK

    Sameer Singh

  2. Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

    Nabeel Murshed

  3. Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Favoritenstr. 9/2/4, 1040, Wien, Austria

    Walter Kropatsch

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© 2001 Springer-Verlag Berlin Heidelberg

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Ezhov, A. (2001). Pattern Recognition with Quantum Neural Networks. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_7

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  • DOI: https://doi.org/10.1007/3-540-44732-6_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41767-5

  • Online ISBN: 978-3-540-44732-0

  • eBook Packages: Springer Book Archive

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