Название: Hybrid Quantum Metaheuristics: Theory and ApplicationsАвтор: Siddhartha Bhattacharyya, Mario Koppen, Elizabeth Behrman, Ivan Cruz-Aceves
Издательство: CRC Press
Серия: Quantum Machine Intelligence
Год: 2022
Страниц: 276
Язык: английский
Формат: pdf (true)
Размер: 15.3 MB
The reference text introduces the principles of quantum mechanics to evolve hybrid metaheuristics-based optimization techniques useful for real world engineering and scientific problems.
A metaheuristic is a heuristic (partial search) algorithm that is more or less an efficient optimization algorithm to real-world problems. Hybrid metaheuristics refer to a proper and judicious combination of several other metaheuristics and machine learning algorithms. The hybrid metaheuristics have been found to be more robust and failsafe owing to the complementary character of the individual metaheuristics in the resultant combination. This is primarily due to the fact that the vision of hybridization is to combine different metaheuristics such that each of the combination supplements the other in order to achieve the desired performance. Typical examples use fuzzy-evolutionary, neuro-evolutionary, neuro-fuzzy evolutionary, rough-evolutionary approaches to name a few. Recently, chaos theory has also found wide applications in evolving efficient hybrid metaheuristics.
Quantum computer, as the name suggests, principally works on several quantum physical features. These could be used as an immense alternative to today’s apposite computers since they possess faster processing capability (even exponentially) than classical computers. The term quantum computing stems from the synergistic combination of quantum mechanical principles and classical information theory conjoined with principles of computer science. Utilization of the basic features of quantum computing into different evolutionary algorithmic frameworks is foremost part of this research in soft computing discipline. A number of researchers has coupled the underlying principles of quantum computing with various metaheuristic structures to introduce different quantum-inspired algorithmic approaches. The evolution of the quantum computing paradigm has led to the evolution of time efficient and robust hybrid metaheuristics by means of conjoining the principles of quantum mechanics with the conventional metaheuristics, thereby enhancing the real-time performance of the hybrid metaheuristics.
This volume aims to bring together recent advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to hybrid quantum metaheuristics, and their applications to engineering problems. The scope of the volume in essence is confined into but not bounded on introducing different novel hybrid quantum metaheuristics for addressing glaring optimization problems ranging from function optimization, data analysis (both discrete and continuous), system optimization, and signal processing to a host of scientific and engineering applications. It is also aimed to emphasize the effectiveness of the proposed approaches over the state-of-the-art existing approaches by means of illustrative examples and real-life case studies.
This volume comprises nine well-versed chapters on different facets of hybrid quantum metaheuristics along with an introductory and concluding chapters. Quantum-inspired metaheuristics can be described as an integrative algorithmic structure, which are designed by exploiting the basics of quantum computing (QC) and metaheuristics.
The IBM Q Experience is an online platform available in Cloud, which gives users access to a set of IBM’s prototype quantum processors. It was launched by IBM in May 2016. IBM Q is an industry first initiative to build universal quantum computers for business, engineering, and science. This effort includes advancing the entire quantum computing technology stack and exploring applications to make quantum broadly usable and accessible. IBM Q is applicable to solve the most challenging problems in chemistry, optimization, Machine Learning, finance, etc. Many self-explanatory documentations are provided in IBMs website on quantum computing.
All experiments were performed using the Matlab platform using an Intel i3 PC with 4GB of RAM.
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