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Lecture notes in computer science

Qubit Neural Network and Its Efficiency

Noriaki Kouda, Nobuyuki Matsui, H. Nishimura, Ferdinand Peper

Published Jan 1, 2003• 18 citations
Publisher
DOI
10.1007/978-3-540-45226-3_42

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We have proposed a novel neuron model whose states and interactions with other neurons are based on the laws of quantum physics. In our previous works, neural network constricted by this neuron, i.e., qubit neural network, has been shown through various benchmark simulations. As an example of nonholonomic system control, in this report, the swing-up control of inverted pendulum by the qubit neural network is investigated to estimate the efficiencies for more practical applications. For comparison, our qubit neural network and a conventional neural network are evaluated. As a result, we find it is possible for our qubit neural network to swing up and stabilize the inverted pendulum while not for any conventional neural network.
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.
Neural networks have attracted much interest in the two decades for their potential to describe brain function realistically. Quantum computing is a likely candidate for improving the computational efficiency of neural networks, since it has been very successful in doing so for a selected set of computational problems. We have proposed Qubit neural network that is a multilayered neural network composed of Qubit inspired neurons with Quantum Back Propagation learning and confirmed the performance concerning basic benchmark problems such as 4-bit and 6-bit parity check problems. In this paper, we examine this Qubit neural network through more practical problems, for example, the Iris data classification and the night vision processing.