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Parallelized variational quantum classifier with shallow QRAM circuit

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Abstract

In this paper, we present a novel quantum variational circuit, harnessing the capabilities of a pre-determined quantum random access memory (QRAM) circuit to enhance machine learning tasks. Our approach enables parallel training on entire datasets, facilitated by QRAM’s streamlined data loading onto qubits through probability amplitudes. This leads to a logarithmic reduction in the qubit width (number of qubits) concerning data dimension and dataset size. Our model is adaptable to various loss functions, rendering it suitable for binary and multi-class classification tasks. To validate our methodology, we conducted numerical simulations using well-established benchmark datasets, Iris and handwritten digits, with the Pennylane platform. Impressively, our approach yielded high classification accuracy in these illustrations. This work demonstrates promising potential for quantum machine learning applications, especially when dealing with large datasets.

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Acknowledgements

This work was supported by the Funding of National Natural Science Foundation of China (Grant No. 62101270).

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Authors and Affiliations

  1. School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Wenyuan Road, Nanjing, 210023, Jiangsu, China

    Bojia Duan & Xin Sun

  2. College of Pharmaceutical Sciences, Zhejiang University, Yuhangtang Road, Hangzhou, 310058, Zhejiang, China

    Chang-Yu Hsieh

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  1. Bojia Duan
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  2. Xin Sun
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  3. Chang-Yu Hsieh
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Contributions

Bojia Duan and Chang-Yu Hsieh contributed to the study conception and design. Bojia Duan and Xin Sun conducted simulations and analyses. Bojia Duan wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Bojia Duan.

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Duan, B., Sun, X. & Hsieh, CY. Parallelized variational quantum classifier with shallow QRAM circuit. Quantum Inf Process 23, 92 (2024). https://doi.org/10.1007/s11128-024-04295-z

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