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In this paper, we propose a low complexity quantum principal componentanalysis (qPCA) algorithm. Similar to the state-of-the-art qPCA, it achievesdimension reduction by extracting principal components of the data matrix,rather than all components of the data matrix, to quantum registers, so thatsamples of measurement required can be reduced considerably. However, the majoradvantage of our qPCA over the state-of-the-art qPCA is that it requires muchless quantum gates. In addition, it is more accurate due to the simplificationof the quantum circuit. We implement the proposed qPCA on the IBM quantumcomputing platform, and the experimental results are consistent with ourexpectations.

A Low Complexity Quantum Principal Component Analysis Algorithm