Search results for "Generative AI and Quantum Computing"
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Quantum Computing and Artificial Intelligence
This book constitutes the proceedings of the Second International Workshop on Quantum Computing and Artificial Intelligence, QC+AI 2026, which took place in Singapore on January 27, 2026.The 7 full papers included in this book were carefully reviewed and selected from 17 submissions. They deal with up to date topics in quantum optimization and quantum machine learning.
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Distributed Information and Computation in Generic Quantum Systems
This book discusses an integrative approach to the currently high profile topics of artificial intelligence, quantum information, and quantum biology, with applications in the biosciences and the emerging fields of computational phenomenology and basal cognition. Specifically, the book addresses theoretical constructs of artificial intelligence and quantum information within scale-free work space architectures as pertaining to neuroscience and biology as well as to self-organizing systems generally. The past few years have seen a rapid convergence of interests between researchers working in evo-devo biology and neuroscience and those working in AI, machine learning, and the physics of information, with much of this convergence driven by recognition that the Free Energy Principle applies not just to nervous systems, but to physical systems in general. The authors develop a scale-free, minimal architecture that associates a generic semantics with any well-defined physical interaction. The presentation is accessible to a broad audience, including advanced undergraduates. The book is appropriate for students and researchers in AI, the physics of information, and the life sciences, particularly those working in the growing interdisciplinary field of active inference.
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AI and Quantum Network Applications in Business and Medicine
Utilizing artificial intelligence (AI) and quantum network applications may revolutionize both business and medicine, offering opportunities for innovation and efficiency. In business, AI tools in data analytics and quantum computing applications help enhance decision-making, optimize supply chains, and unlock avenues for growth through predictive modeling. In medicine, intelligent technologies provide more precise detection and diagnosis, personalize treatment, and improve drug discovery capabilities. Further integration of both tools into business and medicine is necessary to improve outcomes for various sectors and create new approaches to innovation. AI and Quantum Network Applications in Business and Medicine explores the application of artificial intelligence and quantum computing in business and medical industries. Solutions for disease diagnosis, resource allocation, and effective data analysis are presented using tools like machine learning, quantum networking, and intelligent technology. This book covers topics such as medical diagnosis, deep learning, and trauma responses, and is a useful resource for medical professionals, doctors, scientists, computer engineers, business owners, academicians, and researchers.
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Integration of AI, Quantum Computing, and Semiconductor Technology
The integration of artificial intelligence (AI), quantum computing, and semiconductor technology offers improved innovation to redefine computational power and capabilities. As AI drives advances in machine learning and data processing, quantum computing revolutionizes problem-solving with its ability to handle complex calculations at improved speeds. Advancements in semiconductor technology push the limits of processing efficiency and miniaturization. Continued exploration on this convergence may accelerate breakthroughs in various fields such as cryptography, material science, and healthcare. Integration of AI, Quantum Computing, and Semiconductor Technology explores the intersection of artificial intelligence (AI) and semiconductor technology within the context of quantum computing. It offers a comprehensive analysis of the current advancements, challenges, and potential applications resulting from this convergence. This book covers topics such as cyber security, healthcare monitoring, and machine learning, and is a useful resource for computer engineers, energy scientists, business owners, healthcare administrators, environmental scientists, academicians, and researchers.
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Multidisciplinary Applications of AI and Quantum Networking
The multidisciplinary applications of artificial intelligence (AI) and quantum networking drive transformations across various fields, providing new technology, scientific discoveries, and industry improvements. AI's capability to analyze and interpret data along with quantum networking's secure communication, enables breakthroughs in sectors such as healthcare, finance, and logistics. Continued research into this integration may offer opportunities for innovative models and simulations, improved decision-making, and the development of interdisciplinary collaboration. Multidisciplinary Applications of AI and Quantum Networking explores the application of artificial intelligence and quantum computing across multiple industries, disciplines, and sectors. A variety of intelligent tools and technologies like machine learning, networks, augmented and virtual technology, and artificial intelligence are examined for their usefulness in business, medicine, engineering, marketing, and infrastructure. This book covers topics such as quantum computing, intrusion detection, and web technologies, and is a useful resource for computer engineers, business owners, security professionals, researchers, scientists, and academicians.
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Advances in Quantum Inspired Artificial Intelligence
This book advances the artificial intelligence techniques by strongly inspired from quantum computing. Quantum computing (QC), rooted in the mysterious principles of quantum mechanics, has fascinated scientists, researchers, and visionaries for a long time. Its potential lies in changing how we compute, moving from classical bits to quantum bits (qubits), and unlocking incredible processing capabilities. At the same time, artificial intelligence (AI) has been on a trajectory of rapid advancement, with deep learning becoming proficient at tasks that were once deemed impossible. At this juncture, integrating these two fields through various means can play a pivotal role to bring a technological revolution.
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Generative AI
Generative AI (GenAI) is the hottest new area of AI, accelerating innovations affecting all industries and showcasing AI’s ability to present human-like language capabilities with large language models (LLMs). This book builds a solid foundation for the technology underlying GenAI Models, LLM, and foundation and reasoning models for the No-Code audience by explaining the models' workings in simple English. It then takes the reader on a journey to find out where there are gaps to innovate. Each chapter outlines the state of research in each topic and invites learners to build on this research to think about ethical implications and balance it with innovation to benefit everyone. The author covers the technological, product, and societal impact of Generative AI to give a comprehensive guide for this important technology. This book will help the data scientist, product manager, and business leader alike because it covers the API economy and critical industries impacted and offers a framework to think about automation and jobs impacted and what competencies are needed to excel in GenAI and offers an in-depth guide to build GenAI products and applications.
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Real-World Applications of Quantum Computers and Machine Intelligence
The emergence of quantum computing promises a monumental shift in technological capabilities, poised to revolutionize various fields where traditional computing methods may fall short. Quantum computing's potential spans a wide spectrum of applications, from enhancing cryptography to revolutionizing climate modeling and drug discovery. Major corporations are integrating quantum computing into artificial intelligence research, marking a pivotal shift from traditional computing methods. Real-World Applications of Quantum Computers and Machine Intelligence explores practical examples in quantum computing and machine learning for various industry revolutions. By contrasting quantum computing with conventional data mining systems, this book offers insights into the transformative potential of quantum computing, enabling the development of new techniques for real-time problem-solving and innovation. This book covers topics such as deep neural networks, environmental technologies, and machine learning, and is a useful resource for computer engineers, industry professionals, researchers, academicians, scientists, business owners, and healthcare workers.
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Quantum Machine Learning in Industrial Automation
This book focuses on quantum machine learning that harnesses the collective properties of quantum states, such as superposition, interference, and entanglement, uses algorithms run on quantum devices, such as quantum computers, to supplement, expedite, or support the work performed by a classical machine learning program. The devices that perform quantum computations are known as quantum computers. Quantum computers have the potential to revolutionize computation by making certain types of classically intractable problems solvable. A few large companies and small start-ups now have functioning non-error-corrected quantum computers composed of several tens of qubits, and some of these are even accessible to the public through the cloud. Additionally, quantum simulators are making strides in fields varying from molecular energetics to many-body physics. Most known use cases fit into four archetypes: quantum simulation, quantum linear algebra for AI and machine learning, quantum optimization and search, and quantum factorization. Advantages of quantum computing are many and to list a few, first, they're fast. Ultimately, quantum computers have the potential to provide computational power on a scale that traditional computers cannot ever match. In 2019, for example, Google claimed to carry out a calculation in about 200 seconds that would take a classical supercomputer around 10,000 years. Second, they can solve complex problems. The more complex a problem, the harder it is for even a supercomputer to solve. When a classical computer fails, it's usually because of a huge degree of complexity and many interacting variables. However, due to the concepts of superposition and entanglement, quantum computers can account for all these variables and complexities to reach a solution. Last but not the least, they can run complex simulations. The speed and complexity that quantum computing can achieve means that, in theory, a quantum computer could simulate many intricate systems.
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Quantum Computing from Hopfield Nets
This open access book is meant as a textbook for Computer Science students who are looking for a gentle introduction to the world of quantum computing. More specifically, it is written for readers who have basic knowledge of Artificial Intelligence (AI) and Machine Learning (ML) and have a certain familiarity with search algorithms, optimization techniques, and neural networks. This is not because the authors are interested in Quantum AI or Quantum ML, but because they start from the basic premise that there exists a conceptual bridge between certain AI/ML models and certain quantum computing models. The purpose of this book is therefore 1) to revisit these AI/ML models and their applications, and 2) to build on this familiar foundation to segue into the study of quantum computing and its possible use cases. The presentation is technical but pragmatic and practice oriented. The authors cover theory to the necessary extent but largely proceed in an example-driven manner. Most of the examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.Numerous python/numpy/scipy codes support the mathematical discussion and demonstrate how to put theory into practice, accompanied by exercises for each chapter. Parts of the material were adopted from long running lectures on pattern recognition, on the foundations of quantum computing, and on quantum computing algorithms, which are taught by the authors in the Computer Science master’s program at the University of Bonn.
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Quantum Computing
Quantum computing and algorithms are set to revolutionize information processing. Covering such topics, Quantum Computing: The Future of Information Processing explains its principles, practical applications, and future implications in a clear and accessible manner. The book strives to simplify the essential concepts and practical applications of quantum computing. Its aim is to help students and researchers to apply quantum computing to advance AI and machine learning, cybersecurity, and blockchain. With its emphasis on practical applications, the book covers how quantum computing is changing such fields as:FinanceMedicineBuilt environmentNetworking and communicationsWith extensive real-world case studies and practical implementation guidance, the book is a guide for those seeking to understand how quantum computing is applied in various industries. Its in-depth exploration of quantum computing covers both foundational principles and advanced applications in a single resource, saving readers the need to purchase multiple books. Finally, the book focuses on the future of information processing so that students and researchers can anticipate and prepare for the transformative impact of quantum computing.
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Ai & Quantum Computing For Finance & Insurance: Fortunes And Challenges For China And America
This book offers a framework and analysis for the current technological landscape between the United States and China across the financial and insurance sectors as well as emerging technologies such as AI, Blockchain, Cloud and Data Analytics and Quantum Computing (ABCDQ). Based on original lecture slides used by the authors, the book presents contemporary and critical views of emergent technologies for a wide spectrum of readers from CEOs to university lecturers to students. The narrative aims to help readers upgrade their technology literacy and to overcome the fear of AI posed by our lizard brain.
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Generative AI
From algorithms that draft clinical notes in seconds to autonomous agents that triage emergency room backlogs, artificial intelligence is quietly reshaping every facet of healthcare. Generative AI: Unlocking the Next Chapter in Healthcare is the first comprehensive guide to this revolution—charting how we got here, what’s working now, and where the technology could take us next.Drawing on frontline case studies from leading health systems, biotech labs, and tech giants, the book reveals how large language models, synthetic data engines, and multi agent ecosystems are boosting diagnostic accuracy, accelerating drug discovery, and personalizing patient engagement. It also confronts the hard questions—privacy, bias, liability, and the fear of “machines taking over”—offering clear, actionable frameworks for ethical, human centered deployment.Written for clinicians, executives, innovators, and policy makers and patients alike, this definitive volume distils the expertise of industry pioneers into an accessible roadmap. You’ll explore:• The evolution from early machine learning to today’s generative powerhouses• Real world wins—ambient documentation, AI assisted radiology, virtual nursing, and more• The players shaping the landscape: Epic, Google, Microsoft, NVIDIA, Salesforce, and a wave of insurgent startups• Guardrails that preserve trust, protect privacy, and keep humans in the loop• Bold yet plausible futures—from digital twin medicine to globe-spanning agent networksWhether you’re leading a hospital, building health tech solutions, a tech startup, or simply curious about the future of care, Generative AI: Unlocking the Next Chapter in Healthcare delivers the insight, inspiration, and practical guidance you need to thrive in the era where human empathy and machine intelligence work side by side.
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Generative AI and Generative AI of Things for Sustainable Smart Cities
This pioneering book invites readers on a compelling journey into Generative Artificial Intelligence (AI) and its groundbreaking convergence with AI of Things (AIoT), introducing GAIoT as a transformative frontier in urban computing and intelligence. GAIoT signals a paradigm shift towards more intelligent, self-learning, self-evolving, and context-aware systems—capable of generating adaptive, forward-looking solutions to the complex infrastructuraland environmental challenges confronting sustainable smart cities.With its combination of theoretical depth, applied innovation, and interdisciplinary scope, the book offers a comprehensive examination of deep generative models—namely Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Transformers, and hybrid architectures—and their applications in environmental sustainability, climate resilience, infrastructure optimization, dynamic decision-making, and data-driven urban management and planning. From synthetic data generation, data augmentation, and data imputation to predictive modeling and scenario simulation, GAIoT is driving the next wave of climate-responsive, environmentally conscious smart city innovation.What sets this book apart is its first-of-its-kind focus on GAIoT as a pathbreaking force in shaping the future of sustainable urban development. It delivers actionable insights, conceptual and operational frameworks, case studies, and policy guidance—equipping diverse stakeholders with the tools to build cities that not only respond to change but also anticipate and shape it. Targeting a broad and cross-disciplinary audience, the book shares state-of-the-art research, presents innovative solutions, and forecasts future trends in urban transformation. As both a seminal reference and a practical resource for researchers, technologists, practitioners, professionals, and policymakers, it provides essential guidance for those engaged in advancing the next frontier in urban computing and intelligence.
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Generative AI and Creativity
Generative AI and Creativity: From Theory to Practice is an immersive exploration into the revolutionary domain of generative artificial intelligence. It takes a step-by-step journey starting with the fundamentals of generative AI and progressing to advanced applications. The book unravels the complexities of generative models, delving into key techniques such as generative adversarial networks (GANs) and variational autoencoders (VAE)s, providing a solid foundation for readers to understand the underlying principles.FEATURES• Explores the collaboration between humans and AI in creative processes• Examines generative art movements, graphic design applications, and artist-AI collaborations• Defines creativity in AI, by showcasing case studies and discussing ethical considerationsMoving beyond creative content generation in art and music to practical implementations in industries like healthcare and finance, the book showcases the versatility and transformative potential of generative AI across diverse domains. Readers will gain practical skills through coding examples, case studies, and collaborative projects, empowering them to integrate generative AI into their own work.The book is enriched with stories of innovation, featuring pioneers who have leveraged generative AI to push the boundaries of creativity. Ethical considerations and the societal impact of generative AI are also explored to foster a holistic understanding of the technology.
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Artificial Intelligence and Sustainable Innovation
This book explores how convergence of Agentic AI, quantum technologies and regenerative innovations are shaping modern management.As digital change accelerates, climate issues intensify and the workforce evolves, the book offers a multidisciplinary perspective on the impact of intelligent technologies on leadership, strategy and the socio-technical ecosystem. Its scope on quantum-enhanced decision making, sustainable enterprise development, Industry 5.0, human-AI collaboration and ethical governance cover various applications in climate-resilient agriculture and finance, sustainable supply chains and digital public services, and immersive learning. Placing itself at the crossroads of technology, sustainability and human potential, this book offers a roadmap to the future of management. It suggests that the future of management is not in the technology itself but in the integration of quantum intelligence, regenerative innovations and human-centred design to build adaptive and purposeful organisations.This book is designed for an interdisciplinary readership at the nexus of management, technology and sustainability. It will be a valuable resource to academics, researchers and postgraduate students from disciplines including but not limited to management science, information systems, environmental studies and advanced technologies that will be interested both in a theoretical perspective and practical examples for their academic and teaching purposes. Furthermore, practitioners and innovation managers dealing with the interplay between agentic AI, quantum technologies and regenerative innovation will also benefit from this book.
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Generative AI for Cybersecurity
This book lays a systematic foundation for professionals, researchers, and industry readers who are interested the applications and implications of generative AI for cybersecurity. It covers the latest advances in generative AI and its applications, risks, and opportunities in cybersecurity.The authors first introduce the fundamental background of generative AI, the latest cybersecurity issues, and related potential applications in cybersecurity systems. Following this, they comprehensively review the state-of-the-art research and development, covering various aspects of generative AI applications in this area and related challenges and issues, such as training data availability, computational complexity, generalization to different scenarios, AI governance, quantum-empowered AI and many more. These discussions provide a strong understanding of recent advances in the two fields of generative AI and cybersecurity and the convergence of these domains, which will help readers to shape the field as it matures. Hands-on experiments presented throughout will also give them the practical skills for success. By leveraging its capabilities, readers can overcome challenges, understand the risks, enhance performance, and unlock new opportunities for handling the challenges of cybersecurity with generative AI. Consequently, they will be able to apply their knowledge to utilize generative AI in cybersecurity applications to prevent economic and other losses due to cyber risks such as phishing, fake news, deepfake-based fraud, and other cyberattacks.The contents of this book are appropriate for a wide range of readers from general readers to industry experts and scientists. Because it bridges the gap between generative AI and cybersecurity, experts from both fields will benefit from the information presented within. Students with a background in either area will also benefit from the approach that leads from general to specific applications.
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