Quantum Neural Networks Achieving Quantum Algorithms

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

24 Downloads (Pure)


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
Number of pages13
ISBN (Electronic)978-3-319-78658-2
ISBN (Print)978-3-319-78657-5
Publication statusPublished - 2 May 2018

Publication series

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


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


Dive into the research topics of 'Quantum Neural Networks Achieving Quantum Algorithms'. Together they form a unique fingerprint.

Cite this