Resonant Quantum Principal Component Analysis

TL;DR

This paper introduces a resource-efficient resonant quantum PCA algorithm that uses a single probe qubit to extract principal components, demonstrated experimentally on a 4x4 density matrix with high fidelity.

Contribution

It develops a minimal-resource resonant quantum PCA algorithm requiring only one probe qubit, enabling practical quantum dimension reduction.

Findings

Successfully extracted the first principal component with 86.0% efficiency

Achieved a fidelity of 0.90 in experimental demonstration

Demonstrated potential for quantum AI applications

Abstract

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 4$\times$4 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.