Quantum Computing for Data Scientists: Advanced Algorithms and Machine Learning Techniques

This book provides a rigorous and application-driven treatment of quantum computation as a disruptive paradigm for high-dimensional, data-intensive, and optimization-centric computing. It bridges quantum information science with modern data science by examining how superposition, entanglement, interference, quantum measurement, unitary transformations, and Hilbert-space representations can be exploited to design advanced algorithms for machine learning, artificial intelligence, simulation, cryptography, and large-scale analytics. Covering foundational principles through emerging research directions, the volume explores quantum algorithms for data analysis, quantum machine learning frameworks, variational quantum circuits, quantum neural networks, quantum support vector machines, quantum kernel methods, hybrid quantum-classical architectures, and decision-theoretic quantum feature engineering. It further addresses quantum optimization for NP-hard and combinatorial problems, quantum approaches to complex system simulation, industrial applications across healthcare, logistics, finance, energy, and cybersecurity, as well as quantum data structures and database systems for next-generation information processing.

A distinctive strength of the book is its integration of theoretical formalism with practical data science perspectives. It discusses the promises and constraints of Noisy Intermediate-Scale Quantum devices, including decoherence, circuit depth, trainability, barren plateaus, data encoding bottlenecks, and scalability limitations. The book also evaluates quantum cryptography and post-quantum security in financial data processing, while addressing future trends, governance, and ethical considerations in quantum-enabled artificial intelligence. Designed for researchers, data scientists, engineers, academicians, and advanced students, this book serves as a comprehensive technical roadmap for understanding how quantum computation can transform feature representation, accelerate learning pipelines, enhance optimization, secure digital infrastructures, and redefine computational intelligence in the era of exponentially growing data ecosystems.

• Format: Hardcover
• Language: English
• ISBN: 1041284012
• ISBN13: 9781041284017
• Release Date: January 1
• Publisher: CRC Press
• Length: 394 Pages
• Dimensions: 7.0" x 10.0"