Download or read book Principles Of Quantum Artificial Intelligence Quantum Problem Solving And Machine Learning Second Edition written by Andreas Miroslaus Wichert and published by World Scientific. This book was released on 2020-07-08 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making — the core disciplines of artificial intelligence.Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.

Download or read book Principles of Quantum Artificial Intelligence written by Andreas Wichert and published by World Scientific Publishing Company. This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main principles of quantum computation -- Quantum Fourier transform and Grover search. Then, we indicate how these two principles can be applied to problem solving and finally present a general model of a quantum computer that is based on production systems.

Download or read book Principles of Quantum Artificial Intelligence written by Andreas Wichert and published by . This book was released on 2020-07 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Principles Of Quantum Artificial Intelligence written by Andreas Miroslaus Wichert and published by World Scientific. This book was released on 2013-10-23 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main principles of quantum computation — Quantum Fourier transform and Grover search. Then, we indicate how these two principles can be applied to problem solving and finally present a general model of a quantum computer that is based on production systems.

Download or read book Machine Learning Algorithms for Problem Solving in Computational Applications Intelligent Techniques written by Kulkarni, Siddhivinayak and published by IGI Global. This book was released on 2012-06-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.

Download or read book Supervised Learning with Quantum Computers written by Maria Schuld and published by Springer. This book was released on 2018-08-30 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.

Download or read book Machine Learning with Quantum Computers written by Maria Schuld and published by Springer Nature. This book was released on 2021-10-17 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.

Download or read book Machine Learning For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2021-02-09 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Download or read book From Schr dinger s Equation to Deep Learning A Quantum Approach written by N.B. Singh and published by N.B. Singh. This book was released on with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: "From Schrödinger's Equation to Deep Learning: A Quantum Approach" offers a captivating exploration that bridges the realms of quantum mechanics and deep learning. Tailored for scientists, researchers, and enthusiasts in both quantum physics and artificial intelligence, this book delves into the symbiotic relationship between quantum principles and cutting-edge deep learning techniques. Covering topics such as quantum-inspired algorithms, neural networks, and computational advancements, the book provides a comprehensive overview of how quantum approaches enrich and influence the field of deep learning. With clarity and depth, it serves as an enlightening resource for those intrigued by the dynamic synergy between quantum mechanics and the transformative potential of deep learning.

Download or read book Quantum Machine Learning written by Peter Wittek and published by Academic Press. This book was released on 2014-09-10 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. - Bridges the gap between abstract developments in quantum computing with the applied research on machine learning - Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing - Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research

Download or read book A TEXT BOOK OF CONCEPTS OF ARTIFICIAL INTELLIGENCE written by Mahima Shanker Pandey and published by Academic Guru Publishing House. This book was released on 2024-05-23 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: “A Textbook of Concepts of Artificial Intelligence” serves as an indispensable guide for students, professionals, and enthusiasts eager to delve into the complex areas of AI. Written with precision and expertise, this comprehensive book provides a clear exploration of the fundamental concepts, cutting-edge principles, and practical applications in artificial intelligence. Structured with clarity and pedagogical efficiency, each chapter highlights a spectrum of AI concepts, from the fundamentals of machine learning and neural networks to advanced topics such as natural language processing and robotics. Through a mix of theoretical discourse and real world examples, the book bridges the gap between theoretical understanding and practical implementation, empowering readers to understand complex concepts easily. Emphasizing a practical approach, the textbook integrates practical exercises, coding examples, and case studies to reinforce learning and promote a deeper understanding of AI algorithms and techniques. Readers are guided through the intricacies of AI development, learning to design, implement, and evaluate intelligent systems in a variety of domains. Furthermore, the book explores the ethical implications and social impacts of AI technologies, inspiring critical reflection on the responsible deployment and governance of artificial intelligence in today's world. Written by experienced experts in the field, “A Textbook of Concepts of Artificial Intelligence” is not just a textbook, but a comprehensive resource that equips readers with the knowledge and skills to confidently navigate the dynamic landscape of artificial intelligence. Whether used in academia or industry, this book stands as an indispensable companion for anyone wishing to unlock the potential of AI and contribute meaningfully to its advancement.

Download or read book Dancing with Qubits written by Robert S. Sutor and published by Packt Publishing Ltd. This book was released on 2019-11-28 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the principles and practicalities of quantum computing Key Features Discover how quantum computing works and delve into the math behind it with this quantum computing textbook Learn how it may become the most important new computer technology of the century Explore the inner workings of quantum computing technology to quickly process complex cloud data and solve problems Book DescriptionQuantum computing is making us change the way we think about computers. Quantum bits, a.k.a. qubits, can make it possible to solve problems that would otherwise be intractable with current computing technology. Dancing with Qubits is a quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. From there it moves on to a fuller description of classical computing and the mathematical underpinnings necessary to understand such concepts as superposition, entanglement, and interference. Next up is circuits and algorithms, both basic and more sophisticated. It then nicely moves on to provide a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments will affect you. Really understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is introduced and explained thoroughly, in clear English with helpful examples.What you will learn See how quantum computing works, delve into the math behind it, what makes it different, and why it is so powerful with this quantum computing textbook Discover the complex, mind-bending mechanics that underpin quantum systems Understand the necessary concepts behind classical and quantum computing Refresh and extend your grasp of essential mathematics, computing, and quantum theory Explore the main applications of quantum computing to the fields of scientific computing, AI, and elsewhere Examine a detailed overview of qubits, quantum circuits, and quantum algorithm Who this book is for Dancing with Qubits is a quantum computing textbook for those who want to deeply explore the inner workings of quantum computing. This entails some sophisticated mathematical exposition and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, and computer science.

Download or read book Machine Learning A Journey To Deep Learning With Exercises And Answers written by Andreas Miroslaus Wichert and published by World Scientific. This book was released on 2021-01-26 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.Related Link(s)

Download or read book Quantum Machine Learning written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-06-08 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.

Download or read book Quantum Processes Systems and Information written by Benjamin Schumacher and published by Cambridge University Press. This book was released on 2010-03-25 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new and exciting approach to the basics of quantum theory, this undergraduate textbook contains extensive discussions of conceptual puzzles and over 800 exercises and problems. Beginning with three elementary 'qubit' systems, the book develops the formalism of quantum theory, addresses questions of measurement and distinguishability, and explores the dynamics of quantum systems. In addition to the standard topics covered in other textbooks, it also covers communication and measurement, quantum entanglement, entropy and thermodynamics, and quantum information processing. This textbook gives a broad view of quantum theory by emphasizing dynamical evolution, and exploring conceptual and foundational issues. It focuses on contemporary topics, including measurement, time evolution, open systems, quantum entanglement, and the role of information.

Download or read book Quantum Machine Learning An Applied Approach written by Santanu Ganguly and published by Apress. This book was released on 2021-08-11 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author’s active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. What You will Learn Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive Who This Book Is For Data scientists, machine learning professionals, and researchers

Download or read book Hands On Quantum Machine Learning With Python written by Frank Zickert and published by Independently Published. This book was released on 2021-06-19 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think. Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you'll need to implement real-world solutions. Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner.