Hardware-efficient quantum principal component analysis for medical image recognition  

作　　者：Zidong Lin Hongfeng Liu Kai Tang Yidai Liu Liangyu Che Xinyue Long Xiangyu Wang Yu-ang Fan Keyi Huang Xiaodong Yang Tao Xin Xinfang Nie Dawei Lu 

机构地区：[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,China [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,No.3 Binlang Road,Futian District,Shenzhen 518045,China

出　　处：《Frontiers of physics》2024年第5期227-239,共13页物理学前沿（英文版）

基　　金：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);Guangdong Provincial Key Laboratory(No.2019B121203002);Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110987).

摘　　要：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.

关 键 词：quantum simulation quantum principal component analysis nuclear magnetic resonance 

分 类 号：O413[理学—理论物理] R445[理学—物理]