Resonant quantum principal component analysis
Name
sciadv.abg2589.pdf
Description
Published version
Size
788.98 KB
Format
Adobe PDF
Checksum (MD5)
0439a20723a40d2bd889e7b40c25d584
Author(s)
Li, Zhaokai
Chai, Zihua
Guo, Yuhang
Ji, Wentao
Wang, Mengqi
Shi, Fazhan
Wang, Ya
Lloyd, Seth
Du, Jiangfeng
Date Issued
2021
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Li, Zhaokai, Chai, Zihua, Guo, Yuhang, Ji, Wentao, Wang, Mengqi et al. 2021. "Resonant quantum principal component analysis." Science Advances, 7 (34).
Version
Final published version
Abstract
Principal component analysis (PCA) has been widely adopted to reduce the dimension of data while preserving the information. The quantum version of PCA (qPCA) can be used to analyze an unknown low-rank density matrix by rapidly revealing the principal components of it, i.e., the eigenvectors of the density matrix with the largest eigenvalues. However, because of the substantial resource requirement, its experimental implementation remains challenging. Here, we develop a resonant analysis algorithm with minimal resource for ancillary qubits, in which only one frequency-scanning probe qubit is required to extract the principal components. In the experiment, we demonstrate the distillation of the first principal component of a 4 × 4 density matrix, with an efficiency of 86.0% and a fidelity of 0.90. This work shows the speedup ability of quantum algorithm in dimension reduction of data and thus could be used as part of quantum artificial intelligence algorithms in the future.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
10.1126/SCIADV.ABG2589
