Xin Tao, Che Liangyu, Xi Cheng, Singh Amandeep, Nie Xinfang, Li Jun, Dong Ying, Lu Dawei
Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China.
Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Phys Rev Lett. 2021 Mar 19;126(11):110502. doi: 10.1103/PhysRevLett.126.110502.
Principal component analysis (PCA) is a widely applied but rather time-consuming tool in machine learning techniques. In 2014, Lloyd, Mohseni, and Rebentrost proposed a quantum PCA (qPCA) algorithm [Lloyd, Mohseni, and Rebentrost, Nat. Phys. 10, 631 (2014)NPAHAX1745-247310.1038/nphys3029] that still lacks experimental demonstration due to the experimental challenges in preparing multiple quantum state copies and implementing quantum phase estimations. Here, we propose a new qPCA algorithm using the hybrid classical-quantum control, where parameterized quantum circuits are optimized with simple measurement observables, which significantly reduces the experimental complexity. As one important PCA application, we implement a human face recognition process using the images from the Yale Face Dataset. By training our quantum processor, the eigenface information in the training dataset is encoded into the parameterized quantum circuit, and the quantum processor learns to recognize new face images from the test dataset with high fidelities. Our work paves a new avenue toward the study of qPCA applications in theory and experiment.
主成分分析（PCA）是机器学习技术中一种广泛应用但相当耗时的工具。2014年，劳埃德、莫赫森尼和雷本特罗斯提出了一种量子主成分分析（qPCA）算法[劳埃德、莫赫森尼和雷本特罗斯，《自然·物理学》10，631（2014年）NPAHAX1745 - 247310.1038/nphys3029]，由于在制备多个量子态副本和实现量子相位估计方面存在实验挑战，该算法仍缺乏实验验证。在此，我们提出一种使用经典 - 量子混合控制的新qPCA算法，其中参数化量子电路通过简单的测量可观测量进行优化，这显著降低了实验复杂度。作为PCA的一项重要应用，我们使用来自耶鲁人脸数据集的图像实现了人脸识别过程。通过训练我们的量子处理器，训练数据集中的特征脸信息被编码到参数化量子电路中，并且量子处理器学会以高保真度识别测试数据集中的新脸图像。我们的工作为qPCA在理论和实验方面的应用研究开辟了一条新途径。
