Quantum Deep Learning Neural Networks

  • Abu KamruzzamanEmail author
  • Yousef Alhwaiti
  • Avery Leider
  • Charles C. Tappert
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


This study surveys the current status of Quantum Deep Learning Neural Networks. Exciting breakthroughs may soon bring real quantum neural networks, specifically deep learning neural networks, to reality. Three main obstacles have been limiting quantum growth in the deep learning area, and this study has found that new discoveries have changed these obstacles. The first obstacle was the lack of a real quantum computers to experiment with, not simulators. Several companies have significantly increased their inventory of quantum computers in the last year, including IBM. The second obstacle was the impossibility of training quantum networks, but a new algorithm solves this problem. The third obstacle was that neural networks have nonlinear functions, but that has been solved with a new quantum perceptron. This study explains the historical background briefly for context and understanding, then describes these three major accomplishments that will likely lead to real quantum deep learning neural networks.


Deep learning Quantum computing Neural networks Perceptron 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Abu Kamruzzaman
    • 1
    Email author
  • Yousef Alhwaiti
    • 1
  • Avery Leider
    • 1
  • Charles C. Tappert
    • 1
  1. 1.Seidenberg School of Computer Science and Information SystemsPace UniversityPleasantvilleUSA

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