Аннотация
In terms of combining quantum computing (QC) with machine learning (ML), Quantum Principal Component Analysis (QPCA) is a relatively new field that represents what may be a paradigm shift. QPCA results in an exponential improvement in the speed of both eigen decomposition and the dimension reduction by leveraging quantum mechanical characteristics of superposition, entanglement, and quantum phase estimation (QPE) that ultimately have considerable utility. QPCA allows us to potentially alleviate the limitations of classical principal component analysis (PCA). With the rapid expansion of QC, QPCA will revolutionize the analysis of high-dimensional datasets in many fields, including natural language processing (NLP), genetics, finance, and real-time computer vision. This study aims to consider emerging trends, technical approaches, and open avenues of research in developing the future of QPCA analysis. As part of this, we consider hybrid models, scalable quantum hardware architecture, integration frameworks with conventional systems, and others that will be instrumental in the realization of QPCA.