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.
Similar content being viewed by others
References
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)
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)
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)
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)
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)
Vlaso, A.Y.: Quantum Computations and Images Recognition. arXiv:quant-ph/9703010 (1997)
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)
Latorre, J.I.: Image Compression and Entanglement. arXiv:quant-ph/0510031 (2005)
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)
Ruan, Y., Chen, H., Liu, Z., Tan, J.: Quantum image with high retrieval performance. Quantum Inf. Process. 15(2), 637–650 (2016)
Gael, S., Mădălin, G., Gerardo, A.: Quantum learning of coherent states. EPJ Quantum Technol. 2(1), 1–22 (2015)
Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big feature and big data classification. Phys. Rev. Lett. 113(13), 130503 (2014)
Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10(9), 631–633 (2014)
Cai, X., Wu, D., Su, Z., et al.: Entanglement-based machine learning on a quantum computer. Phys. Rev. Lett. 114(11), 110504 (2015)
Aïmeur, E., Brassard, G., Gambs, S.: Quantum speed-up for unsupervised learning. Mach. Learn. 90(2), 261–287 (2013)
Barnett, S.M., Croke, S.: Quantum state discrimination. Adv. Opt. Photonics 1(2), 238–278 (2009)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Advances in Neural Information Processing Systems (2009)
Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103(15), 150502 (2009)
Quantum Tomography. http://en.wikipedia.org/wiki/Quantum_tomography
Kaye, P.: Reversible Addition Circuit Using One Ancillary Bit with Application to Quantum Computing. arXiv:quant-ph/0408173v2 (2004)
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)
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)
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)
Yuan, S., Mao, X., Xue, Y., et al.: SQR: a simple quantum representation of infrared images. Quantum Inf. Process. 13(6), 1353–1379 (2014)
Schützhold, R.: Pattern recognition on a quantum computer. Phys. Rev. A. 67(6), 062311(1–6) (2003)
Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum systems. Quantum Inf. Process. 9(1), 1–11 (2010)
Caraiman, S., Manta, V.I.: Histogram-based segmentation of quantum images. Theor. Comput. Sci. 529, 4660 (2014)
Zhang, Y., Lu, K., Gao, Y., et al.: A novel quantum representation for log-polar images. Quantum Inf. Process. 12(8), 3103–3126 (2013)
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ruan, Y., Chen, H., Tan, J. et al. Quantum computation for large-scale image classification. Quantum Inf Process 15, 4049–4069 (2016). https://doi.org/10.1007/s11128-016-1391-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11128-016-1391-z