Hardware-efficient quantum principal component analysis for medical image recognition

Lin，Zidong1; Liu，Hongfeng1; Tang，Kai1; Liu，Yidai2; Che，Liangyu1; Long，Xinyue1; Wang，Xiangyu1; Fan，Yu Ang1; Huang，Keyi1; Yang，Xiaodong1,3,4; Xin，Tao1,3,4; Nie，Xinfang1,4,5; Lu，Dawei1,3,4,5

2024-10-01

10.1007/s11467-024-1391-x

Frontiers of Physics   影响因子和分区

2095-0462

2095-0470

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. (Figure presented.)

nuclear magnetic resonance quantum principal component analysis quantum simulation

SCI

英语

第一 ; 通讯

2-s2.0-85189649284

Scopus

1.Shenzhen Institute for Quantum Science and Engineering and Department of Physics,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Physics,Hong Kong University of Science and Technology,ClearWaterBay, Kowloon,Hong Kong
3.International Quantum Academy,Shenzhen,518055,China
4.Guangdong Provincial Key Laboratory of Quantum Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
5.Quantum Science Center of Guangdong–HongKong–Macao Greater Bay Area,Shenzhen–HongKong International Science and Technology Park,Shenzhen,No. 3 Binlang Road, Futian District,518045,China

Lin，Zidong,Liu，Hongfeng,Tang，Kai,et al. Hardware-efficient quantum principal component analysis for medical image recognition[J]. Frontiers of Physics,2024,19(5).

Lin，Zidong.,Liu，Hongfeng.,Tang，Kai.,Liu，Yidai.,Che，Liangyu.,...&Lu，Dawei.(2024).Hardware-efficient quantum principal component analysis for medical image recognition.Frontiers of Physics,19(5).

Lin，Zidong,et al."Hardware-efficient quantum principal component analysis for medical image recognition".Frontiers of Physics 19.5(2024).