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Image classification based on quantum K-Nearest-Neighbor algorithm

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Abstract

Image classification is an important task in the field of machine learning and image processing. However, common classification method, the K-Nearest-Neighbor algorithm, has high complexity, because its two main processes: similarity computing and searching, are time-consuming. Especially in the era of big data, the problem is prominent when the amount of images to be classified is large. In this paper, we try to use the powerful parallel computing ability of quantum computers to optimize the efficiency of image classification. The scheme is based on quantum K-Nearest-Neighbor algorithm. Firstly, the feature vectors of images are extracted on classical computers. Then, the feature vectors are inputted into a quantum superposition state, which is used to achieve parallel computing of similarity. Next, the quantum minimum search algorithm is used to speed up searching process for similarity. Finally, the image is classified by quantum measurement. The complexity of the quantum algorithm is only \(O(\sqrt{kM})\), which is superior to the classical algorithms. Moreover, the measurement step is executed only once to ensure the validity of the scheme. The experimental results show that the classification accuracy is \(83.1\%\) on Graz-01 dataset and \(78\%\) on Caltech-101 dataset, which is close to existing classical algorithms. Hence, our quantum scheme has a good classification performance while greatly improving the efficiency.

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Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant Nos. 61502016, 61771230), the Joint Open Fund of Information Engineering Team in Intelligent Logistics (Grant No. LDXX2017KF152) and Shandong Provincial Key Research and Development Program of China (Grant No. 2017CXGC0701).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Yijie Dang, Nan Jiang, Hao Hu & Zhuoxiao Ji

  2. Beijing Key Laboratory of Trusted Computing, Beijing, 100124, China

    Yijie Dang, Nan Jiang, Hao Hu & Zhuoxiao Ji

  3. National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing, 100124, China

    Yijie Dang, Nan Jiang, Hao Hu & Zhuoxiao Ji

  4. School of Information Science and Technology, Linyi University, Linyi, 276000, China

    Wenyin Zhang

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  1. Yijie Dang
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  2. Nan Jiang
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  3. Hao Hu
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  4. Zhuoxiao Ji
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  5. Wenyin Zhang
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Correspondence to Nan Jiang.

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This work is supported by the National Natural Science Foundation of China under Grants Nos. 61502016 and 61771230, the Joint Open Fund of Information Engineering Team in Intelligent Logistics under Grants No. LDXX2017KF152, and Shandong Provincial Key Research and Development Program of China under Grant No. 2017CXGC0701.

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Dang, Y., Jiang, N., Hu, H. et al. Image classification based on quantum K-Nearest-Neighbor algorithm. Quantum Inf Process 17, 239 (2018). https://doi.org/10.1007/s11128-018-2004-9

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