Classical and Quantum Principal Component Analysis in Data Engineering

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.

Abhishek Kumar, PhD is an Assistant Director and Associate Professor in the Computer Science and?Engineering Department at Chandigarh University, Punjab, India with more than 13 years of experience. He has published more than 50 books and more than 170 publications in reputed, peer-reviewed national and international journals, books, and conferences. His research areas include artificial intelligence, renewable energy, image processing, computer vision, data mining, and machine learning.

J.P. Ananth, PhD is a Professor and Dean in the Internal Quality Assurance Cell at Sri Krishna College of Engineering and Technology, Coimbatore, India. His research work has been documented in many journals, and he serves as a reviewer for several international journals and conferences. His research interests include computer vision, pattern recognition, artificial intelligence, and data analytics.

S. Oswalt Manoj, PhD is an Associate Professor in the Department of Computer Science and Engineering at Sri Krishna College of Engineering and Technology, Tamil Nadu, India. He has more than 100 publications in reputed, peer-reviewed national and international journals, books, and conferences, three published books, and ten patents. His research areas include big data analytics, artificial intelligence, computer vision, machine learning, deep learning, and cloud computing.

Navneet Kaur, PhD is a Professor in the Department of Computer Science and Engineering at Chandigarh University, Mohali, India. She has published many research articles in reputed journals, conferences, and book chapters. Her research interests include wireless sensor networks, wireless body area networks, AI, and cloud computing.

A. Jayanthiladevi, PhD is a Professor of Computer Engineering at Marwadi University. With a strong commitment to groundbreaking research, she has published numerous impactful works in international journals and conferences. Her expertise spans computational life sciences, artificial intelligence, mobile communications, machine learning, quantum computing, and health informatics.