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.
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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).
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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
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DOI: https://doi.org/10.1007/s11128-016-1391-z