Principal component analysis has been widely adopted to reduce the dimension of data while preserving information. The quantum version PCA (qPCA) can be used analyze an unknown low-rank density matrix by rapidly revealing principal components it, i.e. eigenvectors with largest eigenvalues. However, due substantial resource requirement, its experimental implementation remains challenging. Here, ...
