Quantum-Inspired Neural Networks

Dr. Moolchand Sharma is an Assistant Professor at the Maharaja Agrasen Institute of Technology, GGSIPU Delhi. He has several publications in reputed international journals and conferences, including SCI-indexed and Scopus-indexed journals. He has authored/edited four books, and also has authored/co-authored chapters with in publications from reputed global publishers. His research areas include Artificial Intelligence, Nature-Inspired Computing, Security in Cloud Computing, Machine Learning, and Search Engine Optimization. He is associated with various professional bodies like IEEE, ISTE, IAENG, ICSES, UACEE, Internet Society, and a life member of the Universal Innovators research laboratory, etc. He possesses almost 10 years of teaching experience. He is the co-convener of the ICICC, DOSCI, ICDAM & ICCCN Springer Scopus-indexed conference series and ICCRDA-2020 Scopus-indexed Material Science & Engineering conference series. He is also the organizer and co-convener of the International Conference on Innovations and Ideas towards Patents (ICIIP) series. He is also the advisory and TPC committee member of the ICCIDS-2022 SSRN Conference. He is also the reviewer of many reputed journals. He has also served as a session chair in many international springer conferences. He has completed a PhD from DCR University of Science & Technology, Haryana. He completed his postgraduate studies in 2012 at SRM University, NCR/Ghaziabad, India and he graduated in 2010 from KNGD Modi Engineering College, Gautam Buddha Technical University.Dr. Nebojsa Bacanin has a PhD from Faculty of Mathematics, University of Belgrade in 2015 (study program Computer Science, average grade 10,00). He was the vice-dean of the Graduate School of Computer Science and Faculty of Informatics and Computing in Belgrade, Serbia. He currently works as a Full Professor and as a Vice-Rector for Scientific Research at Singidunum University, Belgrade, Serbia. He is involved in scientific research in the field of computer science and his specialty includes artificial intelligence, machine learning, deep learning, stochastic optimization algorithms, swarm intelligence, soft-computing, optimization and modeling, image processing, computer vision and cloud and distributed computing. He actively works in the domain of novel and prospective research field, hybrid methods between machine learning and metaheuristics, where metaheuristics are applied for addressing non-deterministic polynomial hard (NP-hard) challenges from machine learning domain such as hyper-parameters optimization (tuning), training and feature selection. Besides improving machine learning/deep learning models for tackling various practical tasks for classification and regression, his research also involves optimized deep learning models for univariate and multivariate time-series forecasting. Moreover, he is an expert from the area of metaheuristics, and he has been actively doing research in enhancing swarm intelligence, as well as other types of metaheuristics, by incorporating minor changes (e.g., modification in exploitation/exploration expressions, parameters’ adjustments, etc.) and/or major modifications by performing hybridization with other methods (e.g., low-level and high-level hybrid metaheuristics methods). He has been applying his methods to wide variety of practical research areas, e.g., cloud computing scheduling, wireless sensor networks (WSNs) localization, coverage and energy consumption, X-ray images classification, stock price forecasting, portfolio optimization, as well as many others.Dr. Tarik Ahmed Rashid is a Principal Fellow for the Higher Education Authority (PFHEA-UK) and a professor in the Department of Computer Science and Engineering at the University of Kurdistan Hewlêr, Iraq. He pursued his Post-Doctoral Fellowship at the Computer Science and Informatics School, College of Engineering, Mathematical and Physical Sciences, University College Dublin, Ireland. His research areas cover Artificial Intelligence, Nature Inspired Algorithms, Swarm Intelligence, Computational Intelligence, Machine Learning, and Data Mining. Tarik is among the Top 4 researchers in Iraq in the Web of Science-indexed published documents in engineering research filed over 5 years (2019-2023). He is on the prestigious Stanford University list of Top 2% of scientists in the world for 2021, 2022, 2023 and 2024. Tarik is also on the list of top 10 researchers in the Al-Ayen Iraqi Researchers Ranking (2022). AIR-Ranking 2022 is a national ranking organized by Al-Ayen University. His team has designed some single and multi-objective optimization algorithms, such as Fitness Dependent Optimizer (FDO), Child Drawing Development Optimization (CDDO), Donkey and smuggler optimization (DSO), Ant Nesting Algorithm (ANA), FOX Algorithm (FOX), Learner Performance based Behavior (LPB), Goose Algorithm (Goose), Lagrange Elementary Optimization (Leo), Shrike Optimization Algorithm (SHOA), Evolutionary Clustering Algorithm Star (ECA*), and Improved Evolutionary Clustering Algorithm Star (iECA*). His team also has designed several multi objective optimization algorithms, such as Multi-Objective Fitness Dependent Optimizer (MOFDO), Multi-objective Learner Performance based Behavior (MOLPB), and Multi-objective Ant Nesting Algorithm (ANA), and Grid Multi-objective Cat Swarm Optimization (GMOCSO).

• Preface. 1. Fundamentals of Quantum-Inspired Neural Computing. 2. Quantum-Inspired Neural Networks for Cybersecurity: Advanced Threat Detection and Defense Mechanisms. 3. Fundamentals of Quantum Computing and Neural Networks. 4. Quantum-Inspired Computing: Classical Approaches to Machine Learning. 5. Concrete Cryptanalysis of LWE: Advances, Benchmarks, and Experimental Realities. 6. Ethical and societal implications of Quantum inspired AI: Aristotelian moral philosophy perspective. 7. Quantum-Inspired Approaches to Computer Vision: Current State and Future Prospects. 8. Quantum-Inspired Approaches in Healthcare and Bioinformatics. 9. Quantum Neural Networks: Bridging Quantum Computing and Artificial Intelligence. 10. Next-Generation Optimization in Quantum and Hybrid Neural Frameworks. 11. Quantum-Inspired Child-Drawing Optimization for Efficient Graph Neural Network Training. Index.