Multilevel Quantum Metaheuristics Applications in Data Exploration

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.

Dettagli sul prodotto

• Con la collaborazione di: Bhattacharyya Siddhartha (Editore), Hiranmoy Roy (Editore), Platos Jan (Editore), Leo Mršić (Editore), Balamurugan Balusamy (Editore)
• Editore: Elsevier
• Contenuto: Libro
• Forma del prodotto: Tascabile
• Data pubblicazione: 23.01.2026
• Categoria: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica
• EAN: 9780443331367
• ISBN: 978-0-443-33136-7
• Dimensioni (della confezione): 15.2 x 22.9 cm
• Peso (della confezione): 450 g
• Serie: Hybrid Computational Intelligence for Pattern Analysis and Understanding
• Categorie: Optimization, Artificial Intelligence, Cloud Computing, Quantum Computing, Artificial Intelligence (AI), Medical imaging, Remote sensing, Image processing, Expert systems / knowledge-based systems, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Artificial Intelligence / Expert Systems, Noise reduction, Distributed systems, drug discovery, quantum algorithms, Feature Extraction, Convolutional Neural Networks, resource allocation, Drug Design, Portfolio Optimization, evolutionary algorithm, combinatorial optimization, metaheuristics, signal analysis, genetic algorithm, anomaly detection, Task Allocation, Edge Detection, Quantum machine learning, Metaheuristic Algorithms, multiresolution analysis, Flower Pollination Algorithm, load balancing, Data Exploration, Natural computing / Quantum computing, Target Identification, Dynamic Environments, neural architecture search, quantum optimization techniques, Quantum game theory, unsupervised clustering, task scheduling, quantum-inspired algorithms, Lead compound, multilevel optimization, Multilevel quantum-inspired techniques, Quantum metaheuristics, Scheduling efficiency, Quantum-inspired ant colony optimization (QiACO), Quantum-inspired particle swarm optimization (QiPSO), Multilevel image segmentation, Time-constrained optimization, Quantum swarm intelligence, Hybrid quantum–classical algorithms, Quantum genetic algorithm, Multilevel quantum metaheuristics, Quantum-inspired genetic algorithm, Learning-based algorithm, Hadamard Gate, Multilevel quantum states, Adaptive heuristics, Molecular qudits, Rotation gate, Quantum encoding, Scheduling under uncertainty, Multilevel quantum computing