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Compact data encoding for data re-uploading quantum classifier

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Abstract

In the realm of quantum machine learning, different genres of quantum classifiers have been designed to classify classical data. Recently, a quantum classifier that features re-uploading the sample to be classified many times along the quantum circuit was proposed. Data re-uploading allows circumventing the limitations established by the no-cloning theorem. This quantum classifier has great potential in NISQ-era, because it requires very few qubits due to the special data encoding scheme it used. Previous work showed that even a single-qubit could constitute effective classifiers for problems with up to 4 dimensions. In this work, we focus our attention on the data encoding scheme of this quantum classifier, we propose an alternative way to encode the input sample in order to reduce by half the number of learnable parameters of the quantum circuit and simplify the computation, so the training time can be greatly shortened. Numerical results show that the new data encoding method achieves higher accuracy for high-dimensional data while using less parameters.

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Notes

  1. An SU(2) unitary can be decomposed into three consecutive rotation gates, for example, \(U=R_z(\theta _1)R_y(\theta _2)R_z(\theta _3)\).

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Acknowledgements

We are very grateful to the reviewers and the editors for their invaluable comments and detailed suggestions that helped to improve the quality of the present paper. This work is supported by Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515011204) and the National Natural Science Foundation of China (No. 61772565).

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

    Liangliang Fan & Haozhen Situ

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  1. Liangliang Fan
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  2. Haozhen Situ
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No data were used during the study. All codes that support the findings of this study are available from the corresponding author upon reasonable request.

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Fan, L., Situ, H. Compact data encoding for data re-uploading quantum classifier. Quantum Inf Process 21, 87 (2022). https://doi.org/10.1007/s11128-022-03429-5

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