Tiansu Chen1,2, Shi bin Zhang1,2, Qirun Wang3, Yan Chang1,2,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2759-2773, 2023, DOI:10.32604/cmc.2023.033284
Abstract 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… More >
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