دانلود کتاب فراابتکاری‌های کوانتومی چندسطحی. کاربردها در کاوش داده‌ها

Quantum computing is fundamentally a synergistic combination of fields from quantum physics, classical information theory, and computer science. Researchers have coupled the underlying principles of quantum computing into various metaheuristic structures to introduce different quantuminspired algorithmic approaches. The prime motivation behind these tasks is to develop algorithms with higher efficiency and usability than existing ones. Quantum Computers principally work on several quantum physical features. These could be an alternative to today’s computers as they possess faster data processing capabilities (even exponentially) than classical computers by resorting to the processing by qubits, which are the fundamental bi-level states in a quantum system. The advantages offered by Quantum Computing become prominent when handling multidimensional data characterized by a huge number of features. A metaheuristic is a heuristic (partial search) algorithm that efficiently optimizes real-world data-intensive problems. Recently, scientists have found that hybrid metaheuristics (a judicious combination of metaheuristics, algorithms from mathematical programming, constraint programming, or machine learning [ML]) are more robust and fail-safe. Hybridization combines different metaheuristics, where the combination supplements others to achieve the desired performance. Typical examples use fuzzy-evolutionary, neuro-evolutionary, neuro-fuzzy evolutionary, and rough-evolutionary approaches, to name a few. Chaos theory has recently found wide applications in evolving efficient hybrid metaheuristics. The advent of the quantum computing paradigm has also given an impetus to evolving time-efficient hybrid quantum metaheuristics, where the principles of quantum mechanics are conjoined successfully to enhance the realtime performance of hybrid metaheuristics. Although the commercial realization of quantum computers relying on multilevel quantum states is still a far cry, scientists have uncovered the mysteries behind multilevel quantum systems characterized by multilevel and higher-level quantum states. These multilevel states, characterized by qudits, offer more robust and fail-safe solutions with far fewer time and space complexities. Hence, the current focus is on evolving multilevel quantum alternatives to the existing bi-level solutions. This book, comprising 13 contributory chapters apart from the Introductory and Concluding chapters, combines recent advances and trends in methodological approaches, theoretical studies, and mathematical and applied techniques related to multilevel quantum metaheuristics (MQMs) and their applications to engineering and scientific problems. The contributory chapters introduce different novel multilevel incarnations of the quantum metaheuristics for addressing glaring optimization problems ranging from network optimization, data analysis, task scheduling, drug design optimization, air quality prediction, and signal processing. The chapters also emphasize the effectiveness of the proposed approaches over the state-of-the-art alternatives with illustrative examples and real-life case studies. Metaheuristic algorithms have become absolutely necessary in solving complex, high-dimensional optimization problems that depend on traditional mathematical methods. It is inspired by nature and physics; these algorithms offer adaptable and strong strategies for navigating nonlinear, multimodal search spaces encountered across disciplines such as engineering, artificial intelligence (AI), and healthcare. With the advancement in quantum computing, a new class of metaheuristics, which is quantum metaheuristics, has emerged, while integrating quantum principles like superposition and entanglement to enhance the computational performance. These are the extension of binary quantum systems where the multilevel quantum representations offer enhanced encodings and better control over the search process. Chapter 1 explores the convergence of multilevel quantum computing and metaheuristics, highlighting their theoretical foundations, practical significance, and transformative potential in solving real-world optimization problems. Chapter 2 introduces the main concepts and theoretical backgrounds of MQMs, presenting their nature at the cusp of quantum-inspired computing and state-of-the-art optimization. It describes the use of multilevel search structures aimed at exploring rigorously large solution spaces in a hierarchical fashion, while quantum-inspired parts can introduce probabilistic encoding, quantum superposition, and analogs of entanglement to speed up convergence and give diversity to solutions. The chapter further discusses the evolution of metaheuristics from classical ones (genetic algorithms and particle swarms, to name a few) to their quantum counterparts, emphasizing how MQMs can solve the issues of premature convergence, stagnation, and computational burden in data-heavy scenarios.