An Examination of Qubit Neural Network in Controlling an Inverted Pendulum
Neural Processing Letters
Time series forecasting with Qubit Neural Networks
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
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With the development of our highly information-oriented society, there is an increasing demand for large-scale and high-level information processing. Toward this goal, studies have sought to create a new computation principle having an information processing ability exceeding the existing Neumann-type computer, such as the creation of a new computation theory or the integration of the frameworks of existing computation theories. As one such approach, quantum neural computing is considered to be interesting, which integrates neural computing and quantum computation. This paper constructs the feed-forward neural network, which is widely used in practice, based on the qubit neuron model. The 4-bit parity-check problem and the general function identification problem are considered. The performance is compared to the feed-forward network based on the conventional neuron model, and it is shown that the proposed model has a higher performance than the conventional model, using the learning diagram composed of convergence rate and the number of learning iterations. The reason for the better performance is also discussed. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(13): 43–51, 2004; Published online in Wiley InterScience (). DOI 10.1002/scj.10342