Название: Classical and Quantum Principal Component Analysis in Data Engineering
Автор: Abhishek Kumar, J.P. Ananth, S. Oswalt Manoj, Navneet Kaur, A. Jayanthiladevi
Издательство: Wiley-Scrivener
Год: 2026
Страниц: 368
Язык: английский
Формат: epub
Размер: 14.1 MB

This essential resource bridges the gap between classical data limitations and the future of computing, giving you the scalable, quantum-accelerated PCA strategies needed to conquer today’s massive, high-dimensional datasets.

With the rapid growth of big data in fields such as genomics, internet traffic analysis, and social network data, traditional principal component analysis methods have reached their limits in terms of scalability and computational efficiency. This volume delves into cutting-edge advancements in principal component analysis (PCA), particularly focusing on its applications in handling high-dimensional and large-scale datasets. It also provides practical insights into how PCA can be applied to fields such as Machine Learning, bioinformatics, and finance. Through real-world case studies, hands-on examples, and guidance on implementing PCA using modern software tools and libraries, the book presents essential principles in quantum information theory and quantum algorithms, establishing the groundwork necessary to comprehend how quantum computing may expedite and improve PCA procedures. This work examines quantum algorithms for matrix decomposition, analyzes the computational benefits of quantum PCA compared to classical approaches, and showcases real applications in Quantum Machine Learning, encryption, and quantum chemistry. Ultimately, this book will serve as a valuable resource for researchers, students, and professionals looking to the future of high-dimensional data analysis and how to apply efficient, scalable methods to PCA in their work.

Quantum Principal Component Analysis (QPCA) is a revolutionary quantum computing (QC) and machine learning (ML) approach for high-dimensional data-set dimensionality reduction. QPCA extracts dominant features and eigenvectors from complex data distributions at exponential speeds using quantum mechanics’ superposition, entanglement, and quantum parallelism, unlike classical Principal Component Analysis (PCA), which struggles with scalability and efficiency in the age of big QPCA and computes the eigenvalues and eigenvectors of a density value representing a dataset’s covariance structure using quantum methods like quantum-phase estimation. Quantum-enhanced computing lowers computation costs and allows for the analysis of more complex datasets. As part of quantum ML (QML) pipelines, QPCA is one step to inspecting enormous datasets for feature selection, compression, and dimensionality reduction. This chapter discusses the algorithmic stages of QPCA and the theoretical and mathematical aspects of QPCA. Practical IBM Qiskit implementations show near-term quantum device constraints. QPCA is evaluated in genetic data analysis feature extraction, financial system predictive modeling, and computer vision (CV) image recognition. Benchmarking compares QPCA’s accuracy, scalability, and performance against classical PCA under different noise models and data attributes. Limitations on experimental validation include quantum decoherence, qubit scarcity, and efficient quantum data encoding. The chapter finishes with a hybrid quantum-classical model, quantum error prevention, and domain-specific quantum algorithm development. QPCA will allow exceptional scientific discovery, industrial innovation, and artificial intelligence (AI) as QC advances.

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