Quantum Machine Learning (QML): Platform, Tools and Applications, Volume 140 in the Advances in Computers series, explores the intersection of quantum computing and artificial intelligence, highlighting advances that promise to revolutionize computational science. The book introduces foundational concepts in quantum computing and circuits, building toward the practical implementation of quantum machine learning (QML) algorithms. Chapters address challenges such as the gradient vanishing problem in variational quantum circuits and explore powerful optimization methods enabled by quantum mechanics. The volume also covers advanced applications, including quantum approaches to smart grid management, quantum Monte Carlo simulations, and predictive modeling in numerical solvers using quantum neural networks.
Real-world relevance is underscored through discussions of transformative quantum algorithms and their potential to reshape machine learning, enabling unprecedented performance in data analysis, optimization, and beyond.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Preface - p. xiii 8. Chapter One: Quantum algorithm for linear systems - p. 1 9. Chapter Two: The Fundamentals of Quantum Computing: A Journey into the Future - p. 27 10. Chapter Three: Basic circuits for quantum computation - p. 53 11. Chapter Four: Quantum machine learning - p. 65 12. Chapter Five: Addressing the gradient vanishing problem in parameterized quantum circuit training and optimization - p. 113 13. Chapter Six: Quantum optimization techniques and applications - p. 137 14. Chapter Seven: Next-gen smart grids: A quantum approach - p. 173 15. Chapter Eight: Quantum Monte Carlo simulations - p. 215 16. Chapter Nine: Quantum Neural Network-based stabilization parameter for solving Partial Differential Equations - p. 251 17. Chapter Ten: Transforming machine learning: An exploration of quantum algorithms and their applications - p. 271
Quantum Machine Learning (QML): Platform, Tools and Applications, Volume 140 in the Advances in Computers series, explores the intersection of quantum computing and artificial intelligence, highlighting advances that promise to revolutionize computational science. The book introduces foundational concepts in quantum computing and circuits, building toward the practical implementation of quantum machine learning (QML) algorithms. Chapters address challenges such as the gradient vanishing problem in variational quantum circuits and explore powerful optimization methods enabled by quantum mechanics. The volume also covers advanced applications, including quantum approaches to smart grid management, quantum Monte Carlo simulations, and predictive modeling in numerical solvers using quantum neural networks.
Real-world relevance is underscored through discussions of transformative quantum algorithms and their potential to reshape machine learning, enabling unprecedented performance in data analysis, optimization, and beyond.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Preface - p. xiii 8. Chapter One: Quantum algorithm for linear systems - p. 1 9. Chapter Two: The Fundamentals of Quantum Computing: A Journey into the Future - p. 27 10. Chapter Three: Basic circuits for quantum computation - p. 53 11. Chapter Four: Quantum machine learning - p. 65 12. Chapter Five: Addressing the gradient vanishing problem in parameterized quantum circuit training and optimization - p. 113 13. Chapter Six: Quantum optimization techniques and applications - p. 137 14. Chapter Seven: Next-gen smart grids: A quantum approach - p. 173 15. Chapter Eight: Quantum Monte Carlo simulations - p. 215 16. Chapter Nine: Quantum Neural Network-based stabilization parameter for solving Partial Differential Equations - p. 251 17. Chapter Ten: Transforming machine learning: An exploration of quantum algorithms and their applications - p. 271
