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Total Size:
15.7 MB
Info Hash:
1580C6419591E26BBE11B51009CA6646B8D5DF18
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Added:
March 2, 2026, 12:40 p.m.
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(Last updated: March 2, 2026, 12:42 p.m.)
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| Bhattacharyya S. Multilevel Quantum Metaheuristics.Apps in Data Exploration 2026.pdf | 15.7 MB |
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SOURCE: Bhattacharyya S. Multilevel Quantum Metaheuristics.Apps in Data Exploration 2026
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COVER
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MEDIAINFO
Textbook in PDF format
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.
Metaheuristic methods frequently require the selection of various parameters, which influence the overall effectiveness of the chosen technique. Selecting the optimal parameter value can be challenging, and a thorough sensitivity analysis of how parameters affect results can be particularly beneficial. This study uses SALib in Python to examine how various input parameters, including population size, values of and, and cognitive parameters, influence the objective function within the evolutionary methods chosen. By systematically altering these input parameters, their effects on the objective function are analyzed. Monte Carlo simulation and design of experiments have been used for this study.
Provides insights into the future of quantum-inspired optimization by covering recent trends and mathematical techniques
Advances knowledge of evolving time-efficient hybrid quantum algorithms that leverage the processing capabilities of emerging qudit-based paradigms
Presents in-depth analysis of quantum mechanical principles with special reference to multilevel quantum states
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