4.8 Article

Experimental Quantum Principal Component Analysis via Parametrized Quantum Circuits

期刊

PHYSICAL REVIEW LETTERS
卷 126, 期 11, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.126.110502

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资金

  1. National Key Research and Development Program of China [2019YFA0308100]
  2. National Natural Science Foundation of China [12075110, 11975117, 11905099, 11875159, U1801661]
  3. Guangdong Basic and Applied Basic Research Foundation [2019A1515011383]
  4. Guangdong International Collaboration Program [2020A0505100001]
  5. Science, Technology and Innovation Commission of Shenzhen Municipality [ZDSYS20170303165926217, KQTD20190929173815000, JCYJ20200109140803865, JCYJ20170 412152620376, JCYJ20180302174036418]
  6. Guangdong Innovative and Entrepreneurial Research Team Program [2019ZT08C044]
  7. Guangdong Provincial Key Laboratory [2019B12 1203002]
  8. Major Scientific Research Project of Zhejiang Lab [2019 MB0AD01]

向作者/读者索取更多资源

The study proposes a new quantum Principal Component Analysis (qPCA) algorithm using hybrid classical-quantum control, significantly reducing experimental complexity. By training a quantum processor, the algorithm encodes eigenface information from the training dataset and learns to recognize new face images with high fidelities, opening up new avenues for qPCA applications in theory and experiment.
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)] 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.

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