Quantum Neural Networks Achieving Quantum Algorithms

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Abstract

This paper explores the possibility to construct quantum algorithms by means of neural networks endowed with quantum gates evolved to achieve prescribed goals. First tentatives are performed on the well known Deutsch and Deutsch-Jozsa problems. Results are promising as solutions are detected for different sizes and initializations of the problems using a standard evolutionary learning process. This approach is then used to design quantum operators by combining simple quantum operators belonging to a predefined set.

Original languageEnglish
Title of host publicationArtificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers
EditorsMarcello Pelillo, Irene Poli, Debora Slanzi, Roberto Serra, Marco Villani, Andrea Roli
PublisherSpringer
Pages3-15
Number of pages13
Volume830
ISBN (Electronic)978-3-319-78658-2
ISBN (Print)978-3-319-78657-5
DOIs
Publication statusPublished - 2 May 2018

Publication series

NameCommunications in Computer and Information Science
Volume830
ISSN (Print)1865-0929

Keywords

  • Quantum Neural Networks
  • Quantum Algorithms
  • Genetic Algorithm
  • heuristic method

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  • Equipment

  • Activities

    • 1 Participation in workshop, seminar, course

    Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2017)

    Delphine Nicolay (Contributor)

    19 Sep 201721 Sep 2017

    Activity: Participating in or organising an event typesParticipation in workshop, seminar, course

    Cite this

    Nicolay, D., & Carletti, T. (2018). Quantum Neural Networks Achieving Quantum Algorithms. In M. Pelillo, I. Poli, D. Slanzi, R. Serra, M. Villani, & A. Roli (Eds.), Artificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers (Vol. 830, pp. 3-15). (Communications in Computer and Information Science; Vol. 830). Springer. https://doi.org/doi.org/10.1007/978-3-319-78658-2_1