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Link to original content: https://link.springer.com/doi/10.1007/s11128-013-0687-5
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Quantum decision tree classifier

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Abstract

We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can manipulate quantum states. We also propose algorithms constructing the quantum decision tree and searching for a target class over the tree for a new quantum object.

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References

  1. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

  2. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  3. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE T. Pattern Anal. 22, 4–37 (2000)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  5. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  6. Pudenz, K.L., Lidar, D.A.: Quantum adiabatic machine learning. Quantum Inf. Process. 12, 2027–2070 (2013)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  7. Tarrataca, L., Wichert, A.: A quantum production model. Quantum Inf. Process. 1q, 189–209 (2011)

    MathSciNet  MATH  Google Scholar 

  8. Hirsh, H.: A quantum leap for AI. IEEE Intell. Syst. 14, 9–16 (July/August 1999)

  9. Bonner, R., Freivalds, R.: A survey of quantum learning. In: Proceedings of the 3rd Workshop on Quantum Computation and Learning, pp. 106–119 (2002)

  10. Aïmeur, E., Brassard, G., Gambs, S.: Machine learning in a quantum world. Proc. Can. AI 2006, 431–442 (2006)

    MathSciNet  Google Scholar 

  11. Aïmeur, E., Brassard, G., Gambs, S.: Quantum speed-up for unsupervised learning. Mach. Learn. 90, 261–287 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  12. Ezhov, A.A.: Pattern recognition with quantum neural networks. In: Proceedings of Advances in Pattern Recognition, pp. 60–71 (2001)

  13. Ventura, D.: Pattern classification using a quantum system. In: Proceedings of the 6th Joint Conference on Information Science, pp. 537–540 (2002)

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

  15. Schützhold, R.: Pattern recognition on a quantum computer. Phys. Rev. A. 67, 062311 (2003)

    Article  ADS  Google Scholar 

  16. Gambs, S.: Quantum Classification (2008). arXiv: quant-ph/0809.0444

  17. Guta, M., Kotlowski, W.: Quantum learning: asymptoticallly optimal classification of qubit sates. New J. Phys. 12, 123032 (2010)

    Article  MathSciNet  ADS  Google Scholar 

  18. Sasaki, M., Carlini, A.: Quantum learning and universal quantum matching machine. Phys. Rev. A. 66, 022303 (2002)

    Article  MathSciNet  ADS  Google Scholar 

  19. Shi, Y.: Entropy lower bounds of quantum decision tree complexity. Inform. Process. Lett. 81, 23–27 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  20. Buhrman, H., Wolf, R.D.: Complexity measures and decision tree complexity: a survey. Theor. Comput. Sci. 288, 21–43 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  21. Dürr, C., H\(\phi \)yer, P.:. A Quantum Algorithm for Finding the Minium (1996). arXiv: quant-ph/9607014

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Songfeng Lu

  2. Department of Computer Science, University of York, York, YO10 5GH, UK

    Samuel L. Braunstein

Authors
  1. Songfeng Lu
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  2. Samuel L. Braunstein
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Corresponding author

Correspondence to Songfeng Lu.

Additional information

The work is supported by the National Natural Science Foundation of China under Grant No. 61173050.

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Lu, S., Braunstein, S.L. Quantum decision tree classifier. Quantum Inf Process 13, 757–770 (2014). https://doi.org/10.1007/s11128-013-0687-5

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  • DOI: https://doi.org/10.1007/s11128-013-0687-5

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