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Qubit Neural Network: Its Performance and Applications

Qubit Neural Network: Its Performance and Applications

Nobuyuki Matsui, Haruhiko Nishimura, Teijiro Isokawa
Copyright: © 2009 |Pages: 27
ISBN13: 9781605662145|ISBN10: 1605662143|ISBN13 Softcover: 9781616925628|EISBN13: 9781605662152
DOI: 10.4018/978-1-60566-214-5.ch013
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MLA

Matsui, Nobuyuki, et al. "Qubit Neural Network: Its Performance and Applications." Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, IGI Global, 2009, pp. 325-351. https://doi.org/10.4018/978-1-60566-214-5.ch013

APA

Matsui, N., Nishimura, H., & Isokawa, T. (2009). Qubit Neural Network: Its Performance and Applications. In T. Nitta (Ed.), Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters (pp. 325-351). IGI Global. https://doi.org/10.4018/978-1-60566-214-5.ch013

Chicago

Matsui, Nobuyuki, Haruhiko Nishimura, and Teijiro Isokawa. "Qubit Neural Network: Its Performance and Applications." In Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, 325-351. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-214-5.ch013

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Abstract

Recently, quantum neural networks have been explored as one of the candidates for improving the computational efficiency of neural networks. In this chapter, after giving a brief review of quantum computing, the authors introduce our qubit neural network, which is a multi-layered neural network composed of quantum bit neurons. In this description, it is indispensable to use the complex-valued representation, which is based on the concept of quantum bit (qubit). By means of the simulations in solving the parity check problems as a bench mark examination, we show that the computational power of the qubit neural network is superior to that of the conventional complex-valued and real-valued neural networks. Furthermore, the authors explore its applications such as image processing and pattern recognition. Thus they clarify that this model outperforms the conventional neural networks.

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