CNOT-measure quantum neural networks

Martin Lukac, Kamila Abdiyeva, Michitaka Kameyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018
PublisherIEEE Computer Society
Pages186-191
Number of pages6
Volume2018-May
ISBN (Electronic)9781538644638
DOIs
Publication statusPublished - Jul 19 2018
Event48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018 - Linz, Austria
Duration: May 16 2018May 18 2018

Other

Other48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018
CountryAustria
CityLinz
Period5/16/185/18/18

Keywords

  • CNOT logic gate
  • Measurement
  • Quanum neural networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    Lukac, M., Abdiyeva, K., & Kameyama, M. (2018). CNOT-measure quantum neural networks. In Proceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018 (Vol. 2018-May, pp. 186-191). IEEE Computer Society. https://doi.org/10.1109/ISMVL.2018.00040