Advertisement

Quantum Information Processing

, Volume 15, Issue 10, pp 4049–4069 | Cite as

Quantum computation for large-scale image classification

  • Yue Ruan
  • Hanwu Chen
  • Jianing Tan
  • Xi Li
Article

Abstract

Due to the lack of an effective quantum feature extraction method, there is currently no effective way to perform quantum image classification or recognition. In this paper, for the first time, a global quantum feature extraction method based on Schmidt decomposition is proposed. A revised quantum learning algorithm is also proposed that will classify images by computing the Hamming distance of these features. From the experimental results derived from the benchmark database Caltech 101, and an analysis of the algorithm, an effective approach to large-scale image classification is derived and proposed against the background of big data.

Keywords

Quantum image Quantum learning Feature extraction Image classification Schmidt decomposition Hamming distance 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61170321, 61502101), Natural Science Foundation of Jiangsu Province, China (Grant No. BK20140651), Natural Science Foundation of Anhui Province, China (Grant No. 1608085MF129), Research Fund for the Doctoral Program of Higher Education (Grant No. 20110092110024), Foundation for Natural Science Major Program of Education Bureau of Anhui Province (Grant No. KJ2015ZD09) and the open fund of Key Laboratory of Computer Network and Information Integration in Southeast University, Ministry of Education, China (Grant No. K93-9-2015-10C).

References

  1. 1.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  2. 2.
    Zhou, X., Yu, K., Zhang, T., Huang, T.: Image classification using super-vector coding of local image descriptors. In: European Conference on Computer Vision (2010)Google Scholar
  3. 3.
    Fu, Z., Sun, X., Liu, Q., Zhou, L., Shu, J.: Achieving efficient cloud search services: multikeyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans. Commun. E98–B(1), 190–200 (2015)ADSCrossRefGoogle Scholar
  4. 4.
    Xia, Z., Wang, X., Sun, X., Wang, Q.: A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 27(2), 340–352 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Schuld, M., Sinayskiy, I., Petruccione, F.: Quantum computing for pattern classification. In: 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI) and Also Appear in the Springer Lecture Notes in Computer Science 8862 (2014)Google Scholar
  6. 6.
  7. 7.
    Vlaso, A.Y.: Quantum Computations and Images Recognition. arXiv:quant-ph/9703010 (1997)
  8. 8.
    Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. In: Proceedings of SPIE in Quantum Information and Computing (2003)Google Scholar
  9. 9.
    Latorre, J.I.: Image Compression and Entanglement. arXiv:quant-ph/0510031 (2005)
  10. 10.
    Le, P.Q., Dong, F., Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quantum Inf. Process. 10(1), 63–84 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Ruan, Y., Chen, H., Liu, Z., Tan, J.: Quantum image with high retrieval performance. Quantum Inf. Process. 15(2), 637–650 (2016)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Gael, S., Mădălin, G., Gerardo, A.: Quantum learning of coherent states. EPJ Quantum Technol. 2(1), 1–22 (2015)CrossRefGoogle Scholar
  13. 13.
    Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big feature and big data classification. Phys. Rev. Lett. 113(13), 130503 (2014)ADSCrossRefGoogle Scholar
  14. 14.
    Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10(9), 631–633 (2014)CrossRefGoogle Scholar
  15. 15.
    Cai, X., Wu, D., Su, Z., et al.: Entanglement-based machine learning on a quantum computer. Phys. Rev. Lett. 114(11), 110504 (2015)ADSCrossRefGoogle Scholar
  16. 16.
    Aïmeur, E., Brassard, G., Gambs, S.: Quantum speed-up for unsupervised learning. Mach. Learn. 90(2), 261–287 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Barnett, S.M., Croke, S.: Quantum state discrimination. Adv. Opt. Photonics 1(2), 238–278 (2009)CrossRefGoogle Scholar
  18. 18.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  19. 19.
    Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  20. 20.
    Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Advances in Neural Information Processing Systems (2009)Google Scholar
  21. 21.
    Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103(15), 150502 (2009)ADSMathSciNetCrossRefGoogle Scholar
  22. 22.
  23. 23.
    Kaye, P.: Reversible Addition Circuit Using One Ancillary Bit with Application to Quantum Computing. arXiv:quant-ph/0408173v2 (2004)
  24. 24.
    Li, H., Zhu, Q., Lan, S., et al.: Image storage, retrieval, compression and segmentation in a quantum system. Quantum Inf. Process. 12(6), 2269–2290 (2013)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Li, H., Zhu, Q., Li, X., et al.: Multidimensional color image storage, retrieval, and compression based on quantum amplitudes and phases. Inf. Sci. 273, 212–232 (2014)CrossRefGoogle Scholar
  26. 26.
    Zhang, Y., Lu, K., Gao, Y., et al.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Yuan, S., Mao, X., Xue, Y., et al.: SQR: a simple quantum representation of infrared images. Quantum Inf. Process. 13(6), 1353–1379 (2014)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Schützhold, R.: Pattern recognition on a quantum computer. Phys. Rev. A. 67(6), 062311(1–6) (2003)Google Scholar
  29. 29.
    Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum systems. Quantum Inf. Process. 9(1), 1–11 (2010)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Caraiman, S., Manta, V.I.: Histogram-based segmentation of quantum images. Theor. Comput. Sci. 529, 4660 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zhang, Y., Lu, K., Gao, Y., et al.: A novel quantum representation for log-polar images. Quantum Inf. Process. 12(8), 3103–3126 (2013)ADSMathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.School of Computer Science and TechnologyAnhui University of TechnologyMaanshanChina
  3. 3.Key Laboratory of Computer Network and Information IntegrationSoutheast University, Ministry of EducationNanjingChina

Personalised recommendations