A Low Complexity Quantum Principal Component Analysis Algorithm

TL;DR

This paper introduces a low complexity quantum PCA algorithm that reduces quantum gate requirements and improves accuracy, enabling more efficient quantum data analysis with experimental validation on IBM quantum hardware.

Contribution

It presents a novel quantum PCA algorithm with significantly fewer quantum gates and enhanced accuracy compared to existing methods.

Findings

Reduces quantum gate count significantly.

Achieves more accurate principal component extraction.

Experimental validation on IBM quantum platform confirms effectiveness.

Abstract

In this paper, we propose a low complexity quantum principal component analysis (qPCA) algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal components of the data matrix, rather than all components of the data matrix, to quantum registers, so that samples of measurement required can be reduced considerably. However, the major advantage of our qPCA over the state-of-the-art qPCA is that it requires much less quantum gates. In addition, it is more accurate due to the simplification of the quantum circuit. We implement the proposed qPCA on the IBM quantum computing platform, and the experimental results are consistent with our expectations.