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Quantum-Inspired Neural Network with Quantum Weights and Real Weights

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DOI: 10.4236/ojapps.2015.510060    2,664 Downloads   3,215 Views   Citations
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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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Shang, F. (2015) Quantum-Inspired Neural Network with Quantum Weights and Real Weights. Open Journal of Applied Sciences, 5, 609-617. doi: 10.4236/ojapps.2015.510060.

References

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