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Li, P.C. and Li, S.Y. (2008) Learning Algorithm and Application of Quantum BP Neural Networks Based on Universal Quantum gates. Journal of Systems Engineering and Electronics, 19, 167-174.
http://dx.doi.org/10.1016/S1004-4132(08)60063-8

has been cited by the following article:

• TITLE: Quantum-Inspired Neural Network with Quantum Weights and Real Weights

  AUTHORS: Fuhua Shang

  KEYWORDS: Quantum Computing, Quantum Rotation Gate, Quantum-Inspired Neuron, Quantum-Inspired Neural Network

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.5 No.10, October 28, 2015

  ABSTRACT: To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer are composed of the traditional neurons. By employing the back propagation algorithm, the training algorithms are designed. Simulation-based experiments using two application examples of pattern recognition and function approximation, respectively, illustrate the availability of the proposed model.