Quantum-Inspired Neural Network with Quantum Weights and Real Weights

Fuhua Shang

doi:10.4236/ojapps.2015.510060

Home > Journals > Biomedical & Life Sciences | Chemistry & Materials Science > OJAppS

OJAppS> Vol.5 No.10, October 2015

Share This Article:

Open Access

Quantum-Inspired Neural Network with Quantum Weights and Real Weights

Abstract Full-Text HTML XML

Download as PDF (Size:333KB) PP. 609-617

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

Author(s) Leave a comment

Fuhua Shang

Affiliation(s)

School of Computer and Information Technology, Northeast Petroleum University, Daqing, China.

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.

KEYWORDS

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

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

[1]	Benioff, P. (1982) Quantum Mechanical Hamiltonian Models of Turing Machines. Journal of Statistical Physics, 3, 515-546. http://dx.doi.org/10.1007/BF01342185

[2]	Shor, P.W. (1994) Algorithms for Quantum Computation: Discrete Logarithms and Factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science, Santa Fe, 20-22 November 1994, 124-134. http://dx.doi.org/10.1109/SFCS.1994.365700

[3]	Grover, L.K. (1996) A Fast Quantum Mechanical Algorithm for Database Search, Proceedings of the 28th Annual ACM Symposium on the Theory of Computing, Pennsylvania, May 1996, 212-219. http://dx.doi.org/10.1145/237814.237866

[4]	Penrose, R. (1994) Shadows of the Mind: A Search for the Missing Science of Consciousness. Oxford University Press, London, 196-201.

[5]	Penrose, R. (1989) The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. Oxford University Press, London, 89-93.

[6]	Kak, S. (1995) On Quantum Neural Computing. Information Sciences, 83, 143-160. http://dx.doi.org/10.1016/0020-0255(94)00095-S

[7]	Gopathy, P. and Nicolaos B.K. (1997) Quantum Neural Networks (QNN’s): Inherently Fuzzy Feed Forward Neural Networks. IEEE Transactions on Neural Networks, 8, 679-693. http://dx.doi.org/10.1109/72.572106

[8]	Li, P.C., Song, K.P. and Yang, E.L. (2010) Model and Algorithm of Neural Network with Quantum Gated Nodes. Neural Network World, 11, 189-206.

[9]	Adenilton, J., Wilson, R. and Teresa, B. (2012) Classical and Superposed Learning for Quantum Weightless Neural Network, Neurocomputing, 75, 52-60. http://dx.doi.org/10.1016/j.neucom.2011.03.055

[10]	Shafee, F. (2007) Neural Networks with Quantum Gated Nodes. Engineering Applications of Artificial Intelligence, 20, 429-437. http://dx.doi.org/10.1016/j.engappai.2006.09.004

[11]	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

[12]	Zweiri, Y.H., Whidborne, J.F. and Seneviratne, L.D. (2003) A Three-Term Backpropagation Algorithm. Neurocomputing, 50, 305-318. http://dx.doi.org/10.1016/S0925-2312(02)00569-6