5 research outputs found
Resonant Quantum Principal Component Analysis
Principal component analysis 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
largest eigenvalues. However, due to the substantial resource requirement, its
experimental implementation remains challenging. Here, we develop a resonant
analysis algorithm with the 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 44 density matrix, with the efficiency of
86.0% and fidelity of 0.90. This work shows the speed-up ability of quantum
algorithm in dimension reduction of data and thus could be used as part of
quantum artificial intelligence algorithms in the future.Comment: 10 pages, 7 figures, have been waiting for the reviewers' responses
for over 3 month
Experimental violation of the Leggett-Garg inequality with a single-spin system
Investigation the boundary between quantum mechanical description and
classical realistic view is of fundamental importance. The Leggett-Garg
inequality provides a criterion to distinguish between quantum systems and
classical systems, and can be used to prove the macroscopic superposition
state. A larger upper bound of the LG function can be obtained in a multi-level
system. Here, we present an experimental violation of the Leggett-Garg
inequality in a three-level system using nitrogen-vacancy center in diamond by
ideal negative result measurement. The experimental maximum value of
Leggett-Garg function is which exceeds the L\"uders
bound with a level of confidence