Multilevel Quantum Metaheuristics: Applications in Data Exploration explores the most recent advances in hybrid quantum-inspired algorithms. Combining principles of quantum mechanics with metaheuristic techniques for efficient data optimization, this book examines multilevel quantum systems characterized by qudits and higher-level quantum states as more robust alternatives to conventional bilevel quantum approaches. It introduces novel multilevel applications of quantum metaheuristics for addressing optimization problems in areas including function optimization, data analysis, scheduling, and signal processing. The book also showcases real-world examples, case studies, and contributions that emphasize the effectiveness of proposed multilevel techniques over existing bilevel methods. Researchers, professionals, and engineers working on intelligent computing, quantum computing, data processing, clustering, and analysis, and those interested in the synergies between quantum computing, metaheuristics, and multilevel quantum systems for enhanced data exploration and analysis will find this book to be of great value.
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. Dedication - p. v 7. List of contributors - p. xiii 8. Preface - p. xvii 9. Chapter 1. Multilevel quantum metaheuristics: fundamentals and applications - p. 1 10. Chapter 2. Multilevel quantum metaheuristics: applications in data exploration - p. 15 11. Chapter 3. Beyond qubits: exploring molecular qudits for advanced quantum computing - p. 51 12. Chapter 4. Neural architecture search using a quantum genetic algorithm for image classification - p. 67 13. Chapter 5. Revolutionizing network optimization: enhancing efficiency and performance through quantum computing and hybrid quantum–classical approaches - p. 95 14. Chapter 6. Automated cluster number detection in hyperspectral images using a qutrit flower pollination algorithm - p. 121 15. Chapter 7. Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study - p. 155 16. Chapter 8. A study on bi-level quantum-inspired genetic algorithm for gray level image segmentation - p. 205 17. Chapter 9. Quantum-inspired metaheuristics for drug design and discovery - p. 231 18. Chapter 10. Application of quantum metaheuristics techniques in data exploration for Internet of Things environment - p. 265 19. Chapter 11. Task allocation and scheduling - p. 289 20. Chapter 12. Image and signal processing - p. 313 21. Chapter 13. Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health - p. 341 22. Chapter 14. Quantum approaches to task allocation and scheduling: enhancing efficiency and optimization in distributed systems - p. 385 23. Chapter 15. Multilevel quantum metaheuristics: concluding remarks and future directions of research - p. 431 24. Index - p. 435
Multilevel Quantum Metaheuristics: Applications in Data Exploration explores the most recent advances in hybrid quantum-inspired algorithms. Combining principles of quantum mechanics with metaheuristic techniques for efficient data optimization, this book examines multilevel quantum systems characterized by qudits and higher-level quantum states as more robust alternatives to conventional bilevel quantum approaches. It introduces novel multilevel applications of quantum metaheuristics for addressing optimization problems in areas including function optimization, data analysis, scheduling, and signal processing. The book also showcases real-world examples, case studies, and contributions that emphasize the effectiveness of proposed multilevel techniques over existing bilevel methods. Researchers, professionals, and engineers working on intelligent computing, quantum computing, data processing, clustering, and analysis, and those interested in the synergies between quantum computing, metaheuristics, and multilevel quantum systems for enhanced data exploration and analysis will find this book to be of great value.
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. Dedication - p. v 7. List of contributors - p. xiii 8. Preface - p. xvii 9. Chapter 1. Multilevel quantum metaheuristics: fundamentals and applications - p. 1 10. Chapter 2. Multilevel quantum metaheuristics: applications in data exploration - p. 15 11. Chapter 3. Beyond qubits: exploring molecular qudits for advanced quantum computing - p. 51 12. Chapter 4. Neural architecture search using a quantum genetic algorithm for image classification - p. 67 13. Chapter 5. Revolutionizing network optimization: enhancing efficiency and performance through quantum computing and hybrid quantum–classical approaches - p. 95 14. Chapter 6. Automated cluster number detection in hyperspectral images using a qutrit flower pollination algorithm - p. 121 15. Chapter 7. Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study - p. 155 16. Chapter 8. A study on bi-level quantum-inspired genetic algorithm for gray level image segmentation - p. 205 17. Chapter 9. Quantum-inspired metaheuristics for drug design and discovery - p. 231 18. Chapter 10. Application of quantum metaheuristics techniques in data exploration for Internet of Things environment - p. 265 19. Chapter 11. Task allocation and scheduling - p. 289 20. Chapter 12. Image and signal processing - p. 313 21. Chapter 13. Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health - p. 341 22. Chapter 14. Quantum approaches to task allocation and scheduling: enhancing efficiency and optimization in distributed systems - p. 385 23. Chapter 15. Multilevel quantum metaheuristics: concluding remarks and future directions of research - p. 431 24. Index - p. 435
