| Autor: |
Lin, Zidong, Liu, Hongfeng, Tang, Kai, Liu, Yidai, Che, Liangyu, Long, Xinyue, Wang, Xiangyu, Fan, Yu-ang, Huang, Keyi, Yang, Xiaodong, Xin, Tao, Nie, Xinfang, Lu, Dawei |
| Zdroj: |
Frontiers of Physics; Oct2024, Vol. 19 Issue 5, p1-13, 13p |
| Abstrakt: |
Principal component analysis (PCA) is a widely used tool in machine learning algorithms, but it can be computationally expensive. In 2014, Lloyd, Mohseni & Rebentrost proposed a quantum PCA (qPCA) algorithm [Nat. Phys. 10, 631 (2014)] that has not yet been experimentally demonstrated due to challenges in preparing multiple quantum state copies and implementing quantum phase estimations. In this study, we presented a hardware-efficient approach for qPCA, utilizing an iterative approach that effectively resets the relevant qubits in a nuclear magnetic resonance (NMR) quantum processor. Additionally, we introduced a quantum scattering circuit that efficiently determines the eigenvalues and eigenvectors (principal components). As an important application of PCA, we focused on classifying thoracic CT images from COVID-19 patients and achieved high accuracy in image classification using the qPCA circuit implemented on the NMR system. Our experiment highlights the potential of near-term quantum devices to accelerate qPCA, opening up new avenues for practical applications of quantum machine learning algorithms. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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