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Performance Evaluation of Three Kinds of Quantum Optimization

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Advances in Computation and Intelligence (ISICA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4683))

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Abstract

Three kinds of quantum optimizations are introduced in this paper as follows: quantum minimization (QM), neuromprphic quantum-based optimization (NQO), and logarithmic search with quantum existence testing (LSQET). In order to compare their fitting ability among three quantum optimizations, the performance evaluation on these methods is implemented for the application of time series forecast. Finally, based on the predictive accuracy of time series forecast the concluding remark will be made to illustrate and discuss these three quantum optimizations.

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References

  1. Grover, L.K.: A Fast Quantum Mechanical Algorithm for Database Search. In: Proc. 28th Ann. ACM Symp. Theory of Comp., pp. 212–219. ACM Press, New York (1996)

    Google Scholar 

  2. Chang, B.R., Tsai, S.F.: Financial Prediction Applications Using Quantum-Minimized Composite Model ASVR and NGARCH. In: IJCNN 2006. Proc. International Joint Conference on Neural Network, pp. 7283–7290. Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada (2006)

    Google Scholar 

  3. Tank, D.W., Hopfield, J.J.: Simple ’neural’ optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans. Circuits Syst. 36, 533–541 (1986)

    Article  Google Scholar 

  4. Imre, S., Balazs, F.: Quantum computing and Communication- An Engineering Approach, 3rd edn. Wiley, West Sussex, England (2005)

    Google Scholar 

  5. Chang, B.R.: Compensation and Regularization for Improving the Forecasting Accuracy by Adaptive Support Vector Regression. International Journal of Fuzzy System 7(3), 109–118 (2005)

    Google Scholar 

  6. Durr, C., Hoyer, P.: A Quantum Algorithm for Finding the Minimum, http://arxiv.org/abs/quant-ph/9607014

  7. Boyer, M., Brassard, G., Hoyer, P., Tapp, A.: Tight Bounds on Quantum Searching. Fortschritte der Physik 46, 493–505 (1998)

    Article  Google Scholar 

  8. Golub, G.H., van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)

    MATH  Google Scholar 

  9. Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., Preda, D.: A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem. Science 292, 472–475 (2001)

    Google Scholar 

  10. Messiah, A.: Quantum Mechanics. Dover, New York (1999)

    Google Scholar 

  11. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of the National Academy of Sciences of the USA, vol. 79(8), pp. 2554–2558 (1982)

    Google Scholar 

  12. Sandon, P.A.: Logarithmic Search in a Winner-Take-All Network. In: Proc. IEEE International Conference on Neural Network, pp. 454–459. IEEE Computer Society Press, Los Alamitos (1991)

    Chapter  Google Scholar 

  13. Tsai, H.F., Chang, B.R.: Adaptive Support Vector Regression Tuning Composite Model of BWCG and NGARCH for Applications of Time-Series Prediction. Journal of Grey System 9(1), 1–7 (2006)

    Google Scholar 

  14. Deng, J.L.: Control Problems of Grey System. System and Control Letter 1(5), 288–294 (1982)

    Article  MATH  Google Scholar 

  15. Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting & Control. Prentice-Hall, New Jersey (1994)

    MATH  Google Scholar 

  16. Haykin, S.: Neural Networks, 2nd edn. Prentice Hall, New Jersey (1999)

    MATH  Google Scholar 

  17. Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)

    Article  Google Scholar 

  18. Ono, I., Kobayashi, S.: A Real-coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distribution Crossover. In: Proc. 7th International Conf. on Genetic Algorithms, pp. 246–253 (1997)

    Google Scholar 

  19. International Federation of Stock Exchanges, FIBV FOCUS MONTHLY STATISTICS (2005), Available: http://www.fibv.com/WFE/home.Asp?nav=ie

  20. Ljung, G.M., Box, G.E.P.: On a Measure of Lack of Fit in Time Series Models. Biometrika 65, 67–72 (1978)

    Article  Google Scholar 

  21. London International Financial Futures and Options Exchange (LIFFE) (2002), Available: http://www.liffe.com/

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taitung University, Taitung,950, Taiwan

    Bao Rong Chang

  2. Department of International Business, Shu-Te University, Kaohsiung,824, Taiwan

    Hsiu Fen Tsai

Authors
  1. Bao Rong Chang
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  2. Hsiu Fen Tsai
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Editor information

Lishan Kang Yong Liu Sanyou Zeng

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© 2007 Springer-Verlag Berlin Heidelberg

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Chang, B.R., Tsai, H.F. (2007). Performance Evaluation of Three Kinds of Quantum Optimization. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_24

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  • DOI: https://doi.org/10.1007/978-3-540-74581-5_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74580-8

  • Online ISBN: 978-3-540-74581-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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