About the Book
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.
Table of Contents:
1. Multilevel Quantum Metaheuristics: Fundamentals and Applications
2. Multilevel Quantum Metaheuristics and Their Role in Data-Intensive Applications
3. Beyond Qubits: Exploring Molecular Spin Qudits for Advanced Quantum Computing
4. Neural Architecture Search Using a Quantum Genetic Algorithm for Image Classification
5. Revolutionizing Network Optimization: Enhancing Efficiency and Performance through Quantum Computing and Hybrid Quantum-Classical Approaches
6. Automated Cluster Number Detection in Hyperspectral Images Using a Qutrit Flower Pollination Algorithm
7. Multi objective portfolio optimization using multilevel quantum-inspired optimization algorithms: A comparative study
8. A Comprehensive Performance Analysis of the Quantum-Inspired Genetic Algorithm for Image Segmentation
9. Quantum-Inspired Metaheuristics for Drug Design and Discovery
10. Application of Quantum Metaheuristics Techniques in Data Exploration for IoT Environment
11. Task Allocation and Scheduling
12. Image Processing and Signalling in Multilevel Quantum Metaheuristics
13. AQIMLQC : A Framework for Advancing Air Quality Prediction using Quantum-Inspired Metaheuristics on Climate Change to achieve positive health
14. Quantum Approaches to Task Allocation and Scheduling: Enhancing Efficiency and Optimisation in Distributed Systems
15. Multilevel Quantum Metaheuristics: Concluding Remarks and Future Directions of Research
About the Author :
Siddhartha Bhattacharyya is a Senior Researcher in the Faculty of Electrical Engineering and Computer Science of VSB Technical University of Ostrava, Czech Republic. He is also serving as the Scientific Advisor of Algebra University College, Zagreb, Croatia. Prior to this, he served as the Principal of Rajnagar Mahavidyalaya, Rajnagar, Birbhum. He was a professor at CHRIST (Deemed to be University), Bangalore, India, and also served as the Principal of RCC Institute of Information Technology, Kolkata, India. He is the recipient of several coveted national and international awards. He received the Honorary Doctorate Award (D. Litt.) from the University of South America and the SEARCC International Digital Award ICT Educator of the Year in 2017. He was appointed as the ACM Distinguished Speaker for the tenure 2018-2020. He has been appointed as the IEEE Computer Society Distinguished Visitor for the tenure 2021-2023. He has co-authored six books, co-edited 75 books, and has more than 300 research publications in international journals and conference proceedings to his credit. Hiranmoy Roy received a B.E. (CSE) degree from Burdwan University, an M. Tech. (CT) degree and PhD (Engg) from Jadavpur University, India, in 2003, 2009, and 2022, respectively. He is now with the RCC Institute of Information Technology, India, as an Associate Professor and HOD in the Department of IT. He is a lifetime member of IEI and has 21+ years of experience, including teaching. His main focus areas are heterogeneous face recognition, face recognition in the wild, generation of image descriptors, women harassment detection, human emotion recognition, image retrieval, biometric cryptography, optimization in deep learning, etc. The technologies he is dealing with are computer vision, deep learning, and image processing.
Jan Platos received a Ph.D. in computer science in 2010. He became a Full professor in 2021 at the Department of Computer Science. Since 2021, he has been Dean of the Faculty of Electrical Engineering and Computer Science, VSB-TUO. He has co-authored more than 240 scientific articles published in proceedings and journals. His primary fields of interest are machine learning, artificial intelligence, industrial data processing, text processing, data compression, bioinspired algorithms, information retrieval, data mining, data structures, and data prediction.
Leo Mršić is the Vice-Rector for science and research at Algebra University and Vice president for Technological Development at the National Council for Higher Education, Science and Technological Development, Head of BDV i-Silver Data Center Algebra LAB, Zagreb, Croatia. Permanent court expert in finance, accounting, bookkeeping, and informatics (12+ years) with a large number (150+) of successfully completed complex expertise procedures. He is also an IPMA A Certified Project Director with 100+ successfully completed complex projects.
Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor in the Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, blockchain, and data sciences
