Bukhari S Quantum Machine Learning Concepts, Algorithms, and Applications 2026 [TorrentX]

Textbook in PDF format
In the exploration of new frontiers in data-driven solutions, the potential of quantum-enhanced Machine Learning has become too important to overlook. Quantum Machine Learning, though still in its formative stages, holds the promise to tackle some of the most complex problems that lie beyond the reach of classical computing. Quantum Machine Learning: Concepts, Algorithms, and Applications is a guide to understanding such quantum principles as superposition and entanglement and how they can enhance learning algorithms and data-processing capabilities. The book features a carefully structured progression from foundational concepts and core algorithms to application-driven case studies and emerging directions for future exploration.
Implementing quantum neural networks on near-term quantum hardware
Quantum variational optimization for machine learning
Quantum-accelerated neural imputations with large language models
Emerging trends, addressing hardware limitations, algorithm optimization, and ethical considerations
This book serves as both a primer and an advanced guide by providing essential knowledge for understanding and implementing quantum-enhanced AI solutions in various professional contexts. It equips readers to become active participants in the quantum revolution transforming Machine Learning