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Quantum classifiers for domain adaptation

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Abstract

Transfer learning (TL), a crucial subfield of machine learning, aims to accomplish a task in the target domain with the acquired knowledge of the source domain. Specifically, effective domain adaptation (DA) facilitates the delivery of the TL task where all the data samples of the two domains are distributed in the same feature space. In this paper, two quantum implementations of the DA classifier are presented with quantum speedup compared with the classical DA classifier. One implementation, the quantum basic linear algebra subroutines-based classifier, can predict the labels of the target domain data with logarithmic resources in the number and dimension of the given data. The other implementation efficiently accomplishes the DA task through a variational hybrid quantum-classical procedure.

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Acknowledgements

The author would like to thank Xiaoting Wang for constructive discussions. The author also would like to thank the referees for helpful comments on this paper. This work is supported by National Key Research and Development Program of China Grant No. 2018YFA0306703, in part by the National Natural Science Foundation of China under Grant 62271296, in part by Natural Science Basic Research Program of Shaanxi (No. 2021JC-47), in part by Key Research and Development Program of Shaanxi (Program No. 2022GY-436, NO. 2021ZDLGY08-07), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2022JQ-018), and in part by Shaanxi Joint Laboratory of Artificial Intelligence (No. 2020SS-03).

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Authors and Affiliations

  1. Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, 710021, China

    Xi He, Feiyu Du, Mingyuan Xue, Xiaogang Du & Tao Lei

  2. School of Electronic Information and Artifical Intelligence, Shaanxi University of Science and Technology, Xi’an, 710021, China

    Xi He, Feiyu Du, Mingyuan Xue, Xiaogang Du & Tao Lei

  3. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Xi He

  4. Department of Electronic and Electrical Engineering, Brunel University London, Middlesex, Uxbridge, UB8 3PH, UK

    A. K. Nandi

  5. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    A. K. Nandi

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  1. Xi He
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Correspondence to Tao Lei.

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The authors declare that they have no conflict of interest. The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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He, X., Du, F., Xue, M. et al. Quantum classifiers for domain adaptation. Quantum Inf Process 22, 105 (2023). https://doi.org/10.1007/s11128-023-03846-0

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