Quantum Computing for Pattern Classification

  • Maria Schuld
  • Ilya Sinayskiy
  • Francesco Petruccione
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8862)


It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger’s proposal for measuring the Hamming distance on a quantum computer [CA Trugenberger, Phys Rev Let 87, 2001] and discuss its advantages using handwritten digit recognition as from the MNIST database.


Machine learning quantum computing quantum artificial intelligence 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria Schuld
    • 1
  • Ilya Sinayskiy
    • 1
    • 2
  • Francesco Petruccione
    • 1
    • 2
  1. 1.Quantum Research GroupUniversity of KwaZulu-NatalDurbanSouth Africa
  2. 2.National Institute for Theoretical PhysicsDurbanSouth Africa

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