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A multi-classification classifier based on variational quantum computation

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Abstract

The interaction between machine learning and quantum physics has given rise to an emerging frontier of quantum machine learning research. In this line, quantum classifiers have received significant attention recently as a quantum device designed to solve the classification problem in machine learning. In this paper, we propose a new variational quantum multi-class classifier that uses \(log_{2}N \) qubits to represent N labels, converts the labels into different quantum states, and optimizes the circuit parameters by the fidelity between the true label state and the output state. Our method effectively reduces the width of the circuit and lowers the number of auxiliary particles needed from N to \(log_{2}N\). We conducted simulation experiments on several datasets. On the MNIST handwritten digits dataset, we achieved 99.8% accuracy for 4 classifications and 97% for 8 classifications. On the CIFAR-10 dataset, we obtained 85.3% accuracy for 8 classifications. Finally, on the CIFAR-100 dataset, we reached 76% accuracy for 16 classifications.

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Data availability

The data that support the findings of this study are openly available in the MNIST Database of handwritten digits and the CIFAR Image Database at http://yann.lecun.com/exdb/mnist/ and https://www.cs.toronto.edu/~kriz/cifar.html

Code availability

Further implementation details are available from the authors upon request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (62172060), Sichuan Science and Technology Program (2022YFG0316, 2023ZHCG0004) and National Key R &D Plan(2022YFB3304303).

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Authors and Affiliations

  1. College of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu, 610059, China

    Jie Zhou, Dongfen Li, Yuqiao Tan, Xiaolong Yang, Yundan Zheng & Xiaofang Liu

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  1. Jie Zhou
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Correspondence to Dongfen Li.

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Zhou, J., Li, D., Tan, Y. et al. A multi-classification classifier based on variational quantum computation. Quantum Inf Process 22, 412 (2023). https://doi.org/10.1007/s11128-023-04151-6

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