A Learning Model in Qubit Neuron According to Quantum Circuit

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.

Authors and Affiliations

1. Kurume National College of Technology, 1-1-1 Komorino, Kurume, Japan

   Michiharu Maeda & Masaya Suenaga

2. Faculty of Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima, Japan

   Hiromi Miyajima

Editors and Affiliations

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

   Lipo Wang

2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

   Ke Chen

3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

   Yew Soon Ong

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