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Quantum Neural Network with Improved Quantum Learning Algorithm

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Abstract

We present a quantum BP neural network with the universality of single-qubit rotation gate and two-qubit Controlled-NOT gate. Also, we show the process of the BP learning algorithm for the quantum model, and propose an improved BP learning algorithm based on quantum genetic algorithm. The type recognition simulation of the Matlab program shows the efficiencies of the quantum neural network and the improved learning algorithm.

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References

  1. Hsu, K.L., Gupta, H.V., Sorooshian, S.: Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 31(10), 2517–2530 (1995)

    ADS  Google Scholar 

  2. Feng, C.X.J., Gowrisankar, A.C., Smith, A.E., et al.: Practical guidelines for developing BP neural network models of measurement uncertainty data. J. Manuf. Syst. 25(4), 239–250 (2006)

    Google Scholar 

  3. Wang, X.Q.: Study of construction project bidding based on the BP neural network improved by GA. China Civil Eng. J. 40(7), 93–98 (2007)

    Google Scholar 

  4. Gong, K., Guan, J.H., Kim, K., et al.: Iterative PET image reconstruction using convolutional neural network representation. IEEE Transactions on Medical Imaging 38(3), 675–685 (2019)

  5. Giulia, M.: Hopfield neural network. Int. J. Addict. 33(2), 481–488 (1998)

    Google Scholar 

  6. Su, Y.F.: Integrating a scale-invariant feature of fractal geometry into the Hopfield neural network for super-resolution mapping. Int. J. Remote Sens. 40(23), 1–22 (2019)

    ADS  Google Scholar 

  7. Frigura, M., Frigura, M.F., Balcu, I., et al.: Algorithm for solving economical and environmental dispatch problems of thermal power plants. IOP Conf. Series Earth Environ. Sci. 219, 012010 (2019)

    Google Scholar 

  8. Yang, F., Paindavoine, M.: Implementation of an RBF neural network on embedded eystems: real-time face tracking and identity verification. IEEE Trans. Neural Netw. 14(5), 1162–1175 (2003)

    Google Scholar 

  9. Mojtaba. M., Ali, G.: Hybrid routing scheme using imperialist competitive algorithm and RBF neural networks for VANETs. Wirel. Netw. 25(5), 2831–2849 (2019)

    Google Scholar 

  10. Li, J.B., Liu, X.G.: Melt index prediction by RBF neural network optimized with an adaptive new ant colony optimization algorithm. J. Appl. Polymer Sci. 119(5), 3093–3100 (2012)

    Google Scholar 

  11. Ding, D.W., Yao, X.L., Wang, N.: Adaptive synchronization of fractional-order complex-valued uncertainty dynamical network with coupling delay. Int. J. Theor. Phys. 58(7), 2357–2371 (2019)

    MathSciNet  MATH  Google Scholar 

  12. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    ADS  MATH  Google Scholar 

  13. AlDuais, M.S., Yaakub, A.R., Yusoff, N.: Dynamic training rate for backpropagation learning algorithm. IEEE Malaysia International Conference on Communications. 277–282 (2013)

  14. Tian, X.S., Zhang, T.J.: Methods to improve BP neural network. J. Huaihua Univ. 25(2), 126–130 (2006)

    Google Scholar 

  15. Wang, T.Y., Ma, J.F., Cai, X.Q.: The postprocessing of quantum digital signatures. Quant. Inf. Process. 16(1), 19 (2017)

    ADS  MathSciNet  MATH  Google Scholar 

  16. Niu, X.F., Zhang, J.Z., Xie, S.C., Chen, B.Q.: A third-party e-payment protocol based on quantum multi-proxy blind signature. Int. J. Theor. Phys. 57(8), 2563–2573 (2018)

    MathSciNet  MATH  Google Scholar 

  17. Feynman, R.: Simulating physics with computers. Int. J. Theor. Phys. 21(6-7), 467–488 (1982)

    MathSciNet  Google Scholar 

  18. Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: 35th Annual Symposium on Foundations of Computer Science, pp 124–134 (1994)

  19. Grover, L.K.: A fast quantum mechanical algorithm for database search. Proceedings of the 28th Annual ACM Symposium on the Theory of Computing, pp 212–219. ACM Press, New York (1996)

    Google Scholar 

  20. Kak, S.C.: On quantum neural computing. Inform. Sci. 83(3-4), 143–160 (1995)

    Google Scholar 

  21. Zuo, X.G., Zhang, Z.X.: Image compression method based on quantum BP network. Comput. Eng. 38(13), 205–211 (2012)

    Google Scholar 

  22. Sun, W., He, Y.J., Meng, M.: A novel quantum neural network model with variable selection for short term load forecasting. Appl. Mech. Mater. 20, 612–617 (2010)

    ADS  Google Scholar 

  23. Akazawa, M., Tokuda, E., Asahi, N., et al.: Quantum hopfield network using single-electron circuits–a novel hopfield network free from the local-minimum difficulty. Analog Integr. Circ. Sig. Process 24(1), 51–57 (2000)

    Google Scholar 

  24. Behrman, E.C., Gaddam, K., Steck, J.E, et al.: Microtubules as a quantum hopfield network. Frontiers Collection 351–370 (2006)

  25. Qin, X., Ma, Y.Q.: Tricritical points and reentry in the quantum hopfield neural-network model. Commun. Theor. Phys. 34(2), 217–222 (2000)

    ADS  Google Scholar 

  26. Gao, Z.C., Gong, S.R.: License plate character recognition based on neural network with quantum gate. Comput. Eng. 34(23), 227–229 (2008)

    Google Scholar 

  27. Li, S., Zhang, P., Li, B., et al.: Application of universal quantum gate neural network in gear fault diagnosis. China Mech. Eng. 26(6), 773–777 (2015)

    Google Scholar 

  28. Altman, C., Pykacz, J., Roman, R.Z.: Superpositional quantum network topologies. Int. J. Theor. Phys. 43(10), 2029–2040 (2004)

    MathSciNet  MATH  Google Scholar 

  29. Zhou, R.G.: Quantum competitive neural network. Int. J. Theor. Phys. 49(1), 110–119 (2010)

    MATH  Google Scholar 

  30. Karayiannis, N.B.: Purnshothaman. G.: Fuzzy pattern classification using feed forward neural networks with multilevel hidden neurons. IEEE Int. Neural Netw. 5(2), 127–132 (1994)

    Google Scholar 

  31. Matsui, N., Kouda, N., Nishimura, H.: Neural network based on QBP and its performance. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks 3, 247–252 (2000)

    Google Scholar 

  32. Li, P.C., Li, S.Y.: Learning algorithm and application of quantum BP neural networks based on universal quantum gates. J. Syst. Eng. Electron. 19(1), 167–174 (2008)

    MATH  Google Scholar 

  33. Schuld, M., Sinayskiy, I., Petruccione, F.: The quest for a quantum neural network. Quantum Inf. Process 13(11), 2567–2586 (2014)

    ADS  MathSciNet  MATH  Google Scholar 

  34. Yuan, D., Cai, L., Li, M., et al.: Multi-sensor integration based on a new quantum neural network model for land-vehicle navigation. Neuroquantology 16(6) (2018)

  35. Ge, B., Luo, H.B.: Image encryption application of chaotic sequences incorporating quantum keys. Int. J. Autom Comput https://doi.org/10.1007/s11633-019-1173-z (2019)

  36. Niu, X.F., Zhang, J.Z., Xie, S.C.: A quantum multi-proxy blind signature scheme based on entangled four-qubit Cluster state. Commun. Theor. Phys. 70(1), 43–48 (2018)

    ADS  MathSciNet  Google Scholar 

  37. Nielsen, M., Chuang, I.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

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Authors and Affiliations

  1. Teaching and Research Section of Political, PLA Information Engineering University, Luoyang, 471003, Henan, China

    Bu-Qing Chen

  2. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710121, Shaanxi, China

    Xu-Feng Niu

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  1. Bu-Qing Chen
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  2. Xu-Feng Niu
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Correspondence to Bu-Qing Chen.

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Chen, BQ., Niu, XF. Quantum Neural Network with Improved Quantum Learning Algorithm. Int J Theor Phys 59, 1978–1991 (2020). https://doi.org/10.1007/s10773-020-04470-9

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  • DOI: https://doi.org/10.1007/s10773-020-04470-9

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