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Quantum Fuzzy Regression Model for Uncertain Environment

Tiansu Chen1,2, Shi bin Zhang1,2, Qirun Wang3, Yan Chang1,2,*

1 School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, China
2 Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu, 610255, China
3 School of Engineering and Technology, University of Hertfordshire, Hertford, UK

* Corresponding Author: Yan Chang. Email: email

Computers, Materials & Continua 2023, 75(2), 2759-2773.


In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation. The application of the quantum fuzzy regression model to small-scale financial data proves that its accuracy is greatly improved compared with the quantum regression model. Moreover, due to the introduction of quantum computing, the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model. The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data. Thus, it is a new model for efficient and accurate big data processing in uncertain environments.


Cite This Article

T. Chen, S. B. Zhang, Q. Wang, Y. Chang, T. Chen et al., "Quantum fuzzy regression model for uncertain environment," Computers, Materials & Continua, vol. 75, no.2, pp. 2759–2773, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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