Название: Intelligent Quantum Information Processing Автор: Siddhartha Bhattacharyya, Ivan Cruz-Aceves, Arpan Deyasi Издательство: CRC Press Серия: Quantum Machine Intelligence Год: 2024 Страниц: 255 Язык: английский Формат: pdf (true) Размер: 13.3 MB
The book discusses the foundations of intelligent quantum information processing applied to several real-life engineering problems, including intelligent quantum systems, intelligent quantum communication, intelligent process optimization, and intelligent quantum distributed networks.
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” is fundamentally a synergistic combination of thoughts from quantum physics, classical information theory, and Computer Science.
Quantum information processing entails the processing of information represented using qubits, which is the basic element of a quantum computer. Information processing in the quantum domain is centered on qubit encoding of the classical information, using the inherent properties of superposition and coherence followed by some quantum measurement operations to arrive at the classical outcome. In addition, the property of entanglement of the qubits helps in long-haul communication at an enhanced data transfer rate. This increased data transfer rate forms the basis for implementing distributed quantum networks in the near future. In addition, faster quantum communication is imminent due to rapid research on quantum information processing, the possible realization of quantum networks, and quantum internet services. Novel algorithms on quantum key distributions have been proposed to pave the way for a secured and robust communication system, thanks to the progress in research on single photon sources and detectors. Sequential single photon communication leads to quantum cryptography, which not only helps prevent eavesdropping but also forms the backbone for building quantum teleportation networks. Although entangled photon generation at the chip level still remains a challenging proposition, semiconductor-based quantum dot detectors and novel optical fibers can become effective for the practical implementation of encryption algorithms.
In addition to device-level research, scientists have invested their efforts to induce intelligence in quantum information processing in order to make the systems robust, fail-safe, and efficient. Utilization of the basic features of quantum computing in different machine learning algorithms has been explored over the decades, thereby evolving quantum-inspired/quantum computational intelligent algorithms. Starting from evolving quantum neural networks to emulating quantum fuzzy principles to evolving quantum metaheuristics, these have been the trends of research in recent years. The advent of these quantum algorithms has opened up a new era in the field of intelligent information processing, where the principles of quantum mechanics are conjoined successfully to enhance the real-time performance of the existing quantum information processing algorithms.
This volume aims to bring together recent advances and trends in the methodological approaches, theoretical studies, and mathematical and applied techniques related to intelligent quantum information processing and their applications to engineering problems. The scope of the book, in essence, is confined to but not bounded to introducing different novel hybrid quantum computational algorithms for addressing the limitations of the conventional information processing algorithms, including quantum machine learning, quantum key distribution, quantum information processing, quantum encryption algorithms, quantum networks, and quantum knowledge discovery in databases, to name a few. 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 book:
• Showcases a detailed overview of different quantum machine learning algorithmic frameworks. • Presents real-life case studies and applications. • Provides an in-depth analysis of quantum mechanical principles. • Provides a step-by-step guide in the build-up of quantum inspired/quantum intelligent information processing systems. • Provides a video demonstration on each chapter for better understanding.
It will serve as an ideal reference text for graduate students and academic researchers in fields such as electrical engineering, electronics and communication engineering, computer engineering, and information technology.
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