马氏距离
算法
矩阵的特征分解
协方差矩阵
计算机科学
主成分分析
特征向量
协方差
量子算法
稳健主成分分析
CMA-ES公司
量子
数学
协方差矩阵的估计
人工智能
物理
量子力学
统计
作者
Tangyuan Ning,Yichuan Yang
出处
期刊:EPL
[IOP Publishing]
日期:2023-07-01
卷期号:143 (1): 18001-18001
标识
DOI:10.1209/0295-5075/acdff5
摘要
Abstract Performing the eigendecomposition of the covariance matrix of the dataset is of great significance in the field of machine learning. However, classical operations will become time-consuming when involving large data sets. In this paper, in order to address this problem, we design an efficient quantum algorithm to prepare the covariance matrix state by means of quantum amplitude estimation. After that, we research on its application in principal component analysis and Mahalanobis distance calculation. Specifically, we obtain the transformation matrix for quantum principal component analysis based on the singular value estimation algorithm and the amplitude amplification algorithm. Furthermore, we invoke the quantum matrix inversion algorithm to calculate the Mahalanobis distance. The final complexity analysis shows that our proposed algorithms can achieve speedup compared to their classical counterparts under certain conditions.
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