A learning model in qubit neuron according to quantum circuit

  • Authors:

  • Michiharu Maeda;Masaya Suenaga;Hiromi Miyajima

  • Affiliations:

  • Kurume National College of Technology, Kurume, Japan;Kurume National College of Technology, Kurume, Japan;Faculty of Engineering, Kagoshima University, Kagoshima, Japan

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2005

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Abstract

This paper presents a novel learning model in qubit neuron according to quantum circuit and describes the influence to learning with gradient descent by changing the number of neurons. The first approach is to reduce the number of neurons in the output layer for the conventional technique. The second is to present a novel model, which has a 3-qubit neuron including a work qubit in the input layer. For the number of neurons in the output layer, the convergence rate and the average iteration for learning are examined. Experimental results are presented in order to show that the present method is effective in the convergence rate and the average iteration for learning.