Hardware-efficient quantum principal component analysis for medical image recognition

Abstract

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.

Graphical abstract

Keywords

quantum simulation / quantum principal component analysis / nuclear magnetic resonance

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Declarations

The authors declare that they have no competing interests and there are no conflicts.

Electronic supplementary materials

The online version contains supplementary material available at https://doi.org/10.1007/s11467-024-1391-x and https://journal.hep.com.cn/fop/EN/10.1007/s11467-024-1391-x.

Acknowledgements

We thank Y. Peng for helpful discussion and suggestions about CT-image discrimination methods. This work was supported by the National Key Research and Development Program of China (No. 2019YFA0308100), the National Natural Science Foundation of China (Nos. 12075110 and 12104213), the Science, Technology and Innovation Commission of Shenzhen Municipality (Nos. KQTD20190929173815000 and JCYJ20200109140803865), Pengcheng Scholars, Guangdong Innovative and Entrepreneurial Research Team Program (No. 2019ZT08C044), and Guangdong Provincial Key Laboratory (No. 2019B121203002), Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515110987).

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