This book traces the roots of intelligence down to quantum physics, and in particular, to quantum natural computing. The concept of intelligence is formalised in such a way that it can be referred to both human and robots, and therefore, this book is focused on a phenomenology of intelligent behaviour, in general, regardless of its biological or physical origin. The simplest patterns of intelligent behaviour, that are generalisation and abstraction, can be found in classical recurrent nets that simulate static, periodic and chaotic attractors. However, only introduction of quantum recurrent nets (QRN), quantum resonance (QR) phenomenon, and quantum-inspired (iQ) systems opens up a closer look to phenomenological similarity between quantum algorithms and paradigms of intelligent behaviour.
This book traces the roots of intelligence down to quantum physics, and in particular, to quantum natural computing. The concept of intelligence is formalised in such a way that it can be referred to both human and robots, and therefore, this book is focused on a phenomenology of intelligent behaviour, in general, regardless of its biological or physical origin. The simplest patterns of intelligent behaviour, that are generalisation and abstraction, can be found in classical recurrent nets that simulate static, periodic and chaotic attractors. However, only introduction of quantum recurrent nets (QRN), quantum resonance (QR) phenomenon, and quantum-inspired (iQ) systems opens up a closer look to phenomenological similarity between quantum algorithms and paradigms of intelligent behaviour.
Stay ahead of the technological curve with this comprehensive, practical guide that showcases how the fusion of quantum principles and soft computing is delivering transformative solutions across finance, healthcare, and manufacturing. Quantum-Inspired Approaches for Intelligent Data Processing explores the cutting-edge fusion of quantum computing principles and soft computing techniques, unraveling the synergistic potential of these two paradigms. The book uses a comprehensive interdisciplinary approach, delving into the foundations of quantum mechanics and soft computing essentials, including fuzzy logic, genetic algorithms, and neural networks. Distinctive in its practical focus, the book showcases how this integration enhances intelligent data processing across various industries, offering tangible solutions to complex challenges. Through real-world applications, this book illuminates the transformative impact of quantum-inspired soft computing across multiple industries, from finance and healthcare to manufacturing. It incorporates case studies, examples, and market analyses, providing a holistic understanding of the subject and exploring emerging trends, challenges, and future opportunities, making it an invaluable resource for researchers and industrialists navigating the dynamic intersection of quantum computing and soft computing in intelligent data processing.
The rapid convergence of quantum computing, artificial intelligence, and neural network architectures has opened an entirely new frontier in computational intelligence. As classical computing approaches approach their physical and algorithmic limits, Quantum Neural Networks (QNNs) have emerged as a promising paradigm capable of addressing complex, large-scale, and data-intensive problems with unprecedented efficiency. This book, Quantum Neural Networks and Intelligent Computing, is designed to provide a comprehensive, interdisciplinary perspective on this evolving domain. This multi-author volume brings together contributions from academicians, researchers, and industry professionals who possess deep expertise in quantum mechanics, machine learning, intelligent systems, and emerging computational models. The chapters are carefully curated to bridge the gap between theoretical foundations and real-world applications, making the book valuable for students, researchers, educators, and professionals alike. The book begins with foundational concepts of quantum computing and neural networks, gradually progressing toward advanced topics such as quantum learning models, hybrid quantum-classical algorithms, variational quantum circuits, quantum optimization, and intelligent decision-making systems. Special emphasis is placed on practical implementations, algorithmic frameworks, and future-ready applications, including quantum AI, cybersecurity, optimization, pattern recognition, and intelligent automation. A key strength of this book lies in its multi-author perspective, which allows diverse viewpoints, innovative methodologies, and contemporary research insights to be presented in a cohesive manner. Each chapter has been peer-reviewed and structured to ensure clarity, technical depth, and academic rigor while maintaining accessibility for readers from varied backgrounds. This book is intended to serve as: A textbook for undergraduate and postgraduate students, A reference guide for researchers and scholars, A resource manual for professionals working in AI, quantum computing, and intelligent systems. We sincerely hope that this book will inspire readers to explore the immense potential of quantum-enabled intelligence and contribute meaningfully to the advancement of next-generation computing technologies. The editors express their heartfelt gratitude to all contributing authors for their valuable insights, dedication, and scholarly efforts. We also extend our appreciation to the reviewers and publishers for their support in bringing this work to fruition. Finally, we thank the readers for their interest and encourage them to explore, experiment, and innovate in this exciting interdisciplinary field.
The objective of this paper is to relate the concept of intelligence to the first principles of physics, and, in particular, to answer the following question: can AI system composed only of physical components compete with a human? The answer is proven to be negative if the AI system is based only on simulations, and positive if digital devices are included. It has been demonstrated that in mathematical world, the bridge from matter to intelligence requires extension and modification of quantum physics. Such an extension is implemented by quantum neural nets that perform simulations combined with digital punctuations. The universality of this quantum-classical hybrid is in capability to violate the second law of thermodynamics by moving from disorder to order without external resources. This advanced capability that could be accepted as a definition of human-like intelligence is illustrated by examples.
Quantum computing is a branch of computer science derived from the fundamental laws of quantum mechanics, such as state superposition, multi-value logic and destructive measure. An open challenge in itself is to re-think in quantum terms classic problems and solving techniques. Another nature-inspired field is the development of swarm-based robotic applications, where the challenge is catching the fundamental laws governing swarm dynamics, such as pattern formation and target reaching. Here, we review some recent approaches on swarm dynamics, the organizational rules of which are formalized according to quantum computing. In this way, the shades of probability in decision-making for multiple-robot systems can be expressed according to the multi-value logic underlying quantum computing. In our review, specific quantum circuits are sketched to give an idea of how these problems have been faced in computational terms. The article is enriched by references to sonification, a strategy adding one more sensory dimension to data representation, a human-friendly tool to navigate the complexity of swarm-robotic movements in a given arena.This article is part of the theme issue 'The road forward with swarm systems'.
This book chapter explores the emerging field of quantum robotics and its potential to revolutionize the design and functionality of intelligent and adaptive robotic systems. It discusses the fundamental principles of quantum mechanics and their application to robotics, highlighting the advantages that quantum technologies offer in terms of computational power, information processing, and communication. The chapter presents various approaches to incorporating quantum elements into robotics, such as quantum sensors, quantum control systems, and quantum-inspired algorithms. Furthermore, it examines the challenges and opportunities in the development of quantum robotics, including hardware constraints, algorithm design, and the integration of classical and quantum components. Overall, this chapter provides an overview of the state-of-the-art in quantum robotics and identifies future directions for research and development.