Quantum Competitive Neural Network

  • Rigui Zhou


Quantum Neural Network (QNN) is a fledging science built upon the combination of classical neural network and quantum computing. After analyzing of traditional competitive neural network, this paper firstly presents a Quantum Competitive Neural Network (QCNN) that can recognize patterns and classify patterns via quantum competition. Contrasting to the conventional competitive neural network, the storage capacity or memory capacity of the QCNN is exponentially increased by a factor of 2 n , where n is the number of qubit. The QCNN has no weights, does not need to learn and update weights, which accelerates the learning process of the network. Besides, the case analysis validates the feasibility and validity of the QCNN in this paper.

QCNN Operators Pattern storage Pattern competition 


  1. 1.
    Barenco, A., Bennett, C.H., Cleve, R., Di Vincenzo, D.P., Margolus, N., Shor, P.: Elementary gates for quantum computation. Phys. Rev. A 52, 3457 (1995) CrossRefADSGoogle Scholar
  2. 2.
    Duan, L.-M., Guo, G.-C.: Probabilistic cloning and identification of linearly independent quantum states. Phys. Rev. Lett. 80, 4999–5002 (1998) CrossRefADSGoogle Scholar
  3. 3.
    Gupta, S., Zia, R.K.P.: Quantum neural networks. arXiv:quant-ph/0201144 v1, 30 Jan. (2002)
  4. 4.
    Kak, S.C.: On quantum neural computing. Inf. Sci. 83, 143–160 (1995) CrossRefGoogle Scholar
  5. 5.
    Kouda, N., Matsui, N., Nishimura, H.: Image compression by layered quantum neural networks. Neural Process. Lett. 16(1), 67–80 (2002) MATHCrossRefGoogle Scholar
  6. 6.
    Kouda, N., Matsui, N., Nishimura, H., Peper, F.: Qubit Neural Network and Its Learning Efficiency, Neural Computing and Applications. Springer, Berlin (2005). doi: 10.1007/s00521-004-0446-8 Google Scholar
  7. 7.
    Kouda, N., Matsui, N., Nishimura, H., Peper, F.: An examination of qubit neural network in controlling an inverted pendulum. Neural Process. Lett. 22(1), 277–290 (2005) CrossRefGoogle Scholar
  8. 8.
    Li, W.-G.: Entangled neural networks.
  9. 9.
    Li, W.-G.: Quantum neural computing study.
  10. 10.
    Liu, Y., Long, G.L., Sun, Y.: Analytic one-bit and CNOT gate constructions of general n-qubit controlled gates. Int. J. Quantum Inf. 6(3), 447–462 (2008) MATHCrossRefGoogle Scholar
  11. 11.
    Long, G.L., Sun, Y.: Efficient scheme for initializing a quantum register with an arbitrary superposed state. Phys. Rev. A 64, 014303 (2001) CrossRefADSGoogle Scholar
  12. 12.
    Menneer, T.: Quantum artificial neural networks. Ph.D. thesis of The Univ. of Exeter, UK (1998) Google Scholar
  13. 13.
    Menneer, T., Narayanan, A.: Quantum-inspired neural networks. Tech. Rep. R329, Univ. of Exeter (1995) Google Scholar
  14. 14.
    Nie, X., Cao, J.: Multistability of competitive neural networks with time-varying and distributed delays. Nonlinear Anal., Real World Appl. 10, 928–942 (2009) MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Palomo, E.J., Domnguez, E., Luque, R.M., Munoz, J.: A competitive neural network for intrusion detection systems. MCO 2008, CCIS 14, pp. 530–537 (2008) Google Scholar
  16. 16.
    Perus, M.: Neuro-quantum parallelism in brain-mind and computer. Informatica 20, 173–183 (1996) Google Scholar
  17. 17.
    Perus, M., Bischof, H.: Quantum-wave pattern recognition: from simulations towards implementation. arXiv:quant-ph/0303092 v2 27 Mar. (2003)
  18. 18.
    Schutzhold, R.: Pattern recognition on a quantum computer. arXiv:quant-ph/0208063v3 3 Dec. (2002)
  19. 19.
    Shafee, F.: Entangled quantum networks. Technical report. (2002)
  20. 20.
    Trugenberger, C.A.: Quantum pattern recognition, (Invited Talk at the 1st Feynman Festival, Univ. of Maryland, College Park, August 2002), arXiv:quant-ph/0210176
  21. 21.
    Ventura, D.: Implementing competitive learning in a quantum system. In: Proceedings of the International Joint Conference on Neural Networks ({IJCNN}’99), paper 513, 1999 Google Scholar
  22. 22.
    Ventura, D., Martinez, T.R.: Initializing the amplitude distribution of a quantum state. Found. Phys. Lett. 12(6), 547–559 (1999) CrossRefMathSciNetGoogle Scholar
  23. 23.
    Ventura, D., Martinez, T.R.: Quantum associative memory. Inf. Sci. 124, 273–296 (2000) CrossRefMathSciNetGoogle Scholar
  24. 24.
    Zhou, R.G., Ding, Q.: Quantum M-P neural network. Int. J. Theor. Phys. 46(12), 3209–3215 (2007) MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Zhou, R.G., Ding, Q.: Quantum pattern recognition with probability of 100%. Int. J. Theor. Phys. 47(5), 1278–1285 (2008) MATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Zhou, R.G., Jiang, N., Ding, Q.: Model and training of QNN with weight. Neural Process. Lett. 24(3), 261–269 (2006) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.College of Information EngineeringEast China Jiao Tong UniversityNanchangP.R. China
  2. 2.Department of PhysicsTsinghua UniversityBeijingP.R. China
  3. 3.Key Laboratory for Atomic and Molecular Nanosciences, Ministry of EducationTsinghua UniversityBeijingP.R. China

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