Elsevier Probability for Deep Learning Quantum - Morgan Kaufmann

Probability for Deep Learning Quantum bridges two critical frontiers in modern technology by presenting probabilistic methods through a unified Many-Sorted Algebra framework. This book addresses the intersection of deep learning and quantum physics, revealing how probability underpins both fields. Written for researchers and practitioners, it explores quantum measuring, quantum information theory, quantum communication theory, quantum sensing, quantum signal processing, and quantum computing applications. The Many-Sorted Algebra approach provides a common platform for understanding probabilistic foundations across machine learning and quantum technologies, making it an essential resource for anyone working at the convergence of these rapidly evolving domains.