Information Sciences—Intelligent Systems: An International Journal
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Qubit neural network and its learning efficiency
Neural Computing and Applications
An Examination of Qubit Neural Network in Controlling an Inverted Pendulum
Neural Processing Letters
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Time series forecasting with Qubit Neural Networks
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
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We investigate a quantum neural network and discuss its application to controlling systems. First, we consider a multi-layer quantum neural network that uses qubit neurons as its information processing unit. Next, we propose a direct neural network controller using the multi-layer quantum neural network. To improve learning performance, instead of applying a back-propagation algorithm for the supervised training of the multi-layer quantum neural network, we apply a real-coded genetic algorithm. To evaluate the capabilities of the direct quantum neural network controller, we conduct computational experiments controlling a discrete-time nonlinear system and a nonholonomic system (a two-wheeled robot). Experimental results confirm the effectiveness of the real-coded genetic algorithm in training a quantum neural network and prove the feasibility and robustness of the direct quantum neural network controller.