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Model and Training of QNN with Weight

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Abstract

Quantum Neural Network (QNN) is a burgeoning new field built upon the combination of classical neural networks and quantum computations, which has many problems needed to solve. Where the learning of the network weight vector is an issue must be settled to develop QNN. Upon the analysis of the Grover’s quantum algorithm, a model of QNN with weight vector and a training method for it are proposed in this paper. It can be shown that this model and method work in quantum mechanism. Results on the data set show that this network model can deal with some classical problem such as XOR problem and the proposed weight updating algorithm based on the Grover always can learn training examples in a certain percentage, despiting it has not been proven to excel classical learning algorithm in performance. It yet has some advantages over classical counterpart.

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References

  1. Kak S.C. (1995): On quantum neural computing. Information Sciences 83, 143–160

    Article  Google Scholar 

  2. Menneer, T.: Quantum artificial neural networks. Ph. D. thesis, The University of Exeter, UK, 1998.

  3. Menneer, T. and Narayanan, A.: Quantum-inspired neural networks. Techical Report R329, University of Exeter, UK, 1995.

  4. Ventura D., Martinez T.R. (2000): Quantum associative memory. Information Sciences 124, 273–296

    Article  MathSciNet  Google Scholar 

  5. Li, W.: Entangled neural networks.http://www.cic.unb.br/~weigang/qc/enn2000.pdf.

  6. Li, W.: Quantum neural computing study. http://www.cic.unb.br//~weigang/qc/ enn2000.pdf.

  7. Fariel, S.: Entangled quantum networks, Technical Report. http://arxiv.org/ftp/quantph/ papers/0203/0203010.pdf, 2002.

  8. Kouda, N., Matsui N., Nishimura, H. and Peper, F.: Qubit neural network and its efficiency, In: Proceedings of Knowledge-Based Intelligent Information Engineering Systems (KES2003), LNAI-2774, pp. 304–310, Springer-Verlag, Berlin, 2003.

  9. Kouda, N., Matsui, N., Nishimura, H. and Peper, F.: Qubit neural network and its learning efficiency, In: Neural Computing and Applications, Springer-Verlag, Berlin; DOI:10.1007/s00521-004-0446-8, 2005.

  10. Kouda N., Matsui N., Nishimura H., Peper F. (2005): An examination of qubit neural network in controlling an inverted pendulum. Neural Processing Letters 22, 277–290

    Article  Google Scholar 

  11. Kouda N., Matsui N., Nishimura H. (2002): Image compression by layered quantum neural networks. Neural Processing Letters 16(1): 67–80

    Article  MATH  Google Scholar 

  12. Grover, L.: A fast quantum mechanical algorithm for database search, In: Proceedings of the ACM Symposium on the Theory of Computing 212–219, ACM Press, New York, 1996.

  13. Sanjay, G. and Zia, R. K. P.: Quantum neural networks. http://www.arxiv.org/PS _cache/quant-ph/pdf/0201/0201144.pdf.

  14. Xia, P.-S.: Quantum computing, Jounal of Computer Research and Development 138(110) (2001), (in Chinese).

  15. Shiyan, H.: Quantum neural network for image watermarking, Lecture Notes in Computer Science, Springer-Verlag, Berlin, pp. 669–674, 2004.

  16. Ricks, B. and Ventura D.: Training a quantum neural network. Neural Information processing systems, pp. 1019–1026, 2003.

  17. Jean, F., Gilson, A. and Giraldi.: Quantum models for artificial neural network. http://arquivosweb.lncc.br/pdfs/QNN-Review.pdf, 2003.

  18. Laxmidhar, B. and Bharat, S.: Stochastic filtering and speech enhancement using a recurrent quantum neural network. 2004, In: Proceedings of International Conference on Intelligent Sensing and Information Processing, pp. 165–170, IEEE, New York, 2004.

  19. Laxmidhar, B. and Indrani, K.: Quantum stochastic filtering. 2005, In: IEEE International Conference on Systems, Man and Cybernetics, vol. 3, October 10–12, pp. 2161–2167, IEEE, New York, 2005.

  20. Shor, P. W.: Algorithms for quantum omputation: discrete logarithms and factoring, In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science, pp. 124–134. IEEE Computer Society Press, Santa Fe, 1994.

  21. Karayiannis, N. B., Mukherjee, A., Glover, J. R., Frost, J. D., Hrachovy, R. A. Jr. and , E.M.: An evaluation of quantum neural networks in the detection of epileptic seizures in the neonatal electroencephalogram, Soft Computer (2005) DOI 10.1007/s00500-005-0498-4.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 210016, China

    Zhou Rigui, Jiang Nan & Ding Qiulin

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  1. Zhou Rigui
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  2. Jiang Nan
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  3. Ding Qiulin
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Correspondence to Zhou Rigui.

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Rigui, Z., Nan, J. & Qiulin, D. Model and Training of QNN with Weight. Neural Process Lett 24, 261–269 (2006). https://doi.org/10.1007/s11063-006-9025-6

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  • DOI: https://doi.org/10.1007/s11063-006-9025-6

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