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Quantum speedup for pool-based active learning

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Abstract

Active learning aims to select the most informative samples to train an accurate classifier with minimum cost of labeling. It is widely used in many machine learning systems, where there are a large amount of unlabeled data, but it is difficult or expensive to obtain their labels due to the involvement of human efforts. However, active learning is time-consuming, particularly for the applications those have a great number of unlabeled samples, such as image retrieval, text mining and speech recognition. Thus, it is crucial to speed up the active learning algorithm. In this paper, we propose a quantum version of active learning algorithm, which converts a classical active learning to its quantum counterpart. We focus on the pool-based active learning, which is one of the most popular branches of active learning. The proposed quantum active learning algorithm can achieve quadratic speedup over the classical pool-based active learning.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61802061, 61602532, 61772565, 61871205), the Innovation Project of Department of Education of Guangdong Province of China (No. 2017KTSCX180), the Science and Technology Project of Jiangmen City of China (No. 2018JC01019), the Natural Science Foundation of Guangdong Province of China (Grant No. 2017A030313378), the Project of Department of Education of Guangdong Province.(No. 2017KQNCX216), the Science and Technology Program of Guangzhou City of China (No. 201707010194) and the Fundamental Research Funds for the Central Universities (No.17lgzd29).

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China

    Zhimin He

  2. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China

    Lvzhou Li

  3. Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen, 518055, China

    Shenggen Zheng & Xiangfu Zou

  4. Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Shenggen Zheng

  5. School of Mathematics and Computational Science, Wuyi University, Jiangmen, 529020, China

    Xiangfu Zou

  6. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Haozhen Situ

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  1. Zhimin He
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Correspondence to Haozhen Situ.

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He, Z., Li, L., Zheng, S. et al. Quantum speedup for pool-based active learning. Quantum Inf Process 18, 345 (2019). https://doi.org/10.1007/s11128-019-2460-x

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