Quantum Information Processing

, Volume 13, Issue 11, pp 2567–2586 | Cite as

The quest for a Quantum Neural Network

  • Maria Schuld
  • Ilya Sinayskiy
  • Francesco Petruccione


With the overwhelming success in the field of quantum information in the last decades, the ‘quest’ for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. Concentrating on Hopfield-type networks and the task of associative memory, it outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing. It establishes requirements for a meaningful QNN and reviews existing literature against these requirements. It is found that none of the proposals for a potential QNN model fully exploits both the advantages of quantum physics and computing in neural networks. An outlook on possible ways forward is given, emphasizing the idea of Open Quantum Neural Networks based on dissipative quantum computing.


Quantum computing Artificial neural networks Open quantum systems Quantum Neural Networks 



This work is based upon research supported by the South African Research Chair Initiative of the Department of Science and Technology and National Research Foundation.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Maria Schuld
    • 1
  • Ilya Sinayskiy
    • 1
    • 2
  • Francesco Petruccione
    • 1
    • 2
  1. 1.Quantum Research Group, School of Chemistry and PhysicsUniversity of KwaZulu-NatalDurbanSouth Africa
  2. 2.National Institute for Theoretical Physics (NITheP)KwaZulu-NatalSouth Africa

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