Principles Of Quantum Artificial Intelligence Quantum Problem Solving And Machine Learning Second Edition

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.

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. Contents:IntroductionComputationProblem SolvingInformationReversible AlgorithmsProbabilityIntroduction to Quantum PhysicsComputation with QubitsPeriodicitySearchQuantum Problem-SolvingQuantum CognitionRelated Approaches Readership: Professionals, academics, researchers and graduate students in artificial intelligence, theoretical computer science, quantum physics and computational physics. Keywords:Quantum Computing;Quantum Theory;Artificial Intelligence;Cognitive Computation;AlgorithmsKey Features:Introduces a new subarea of AI — Quantum Artificial IntelligenceOrients itself on computer science by merging AI and Quantum Computation principles

Dive into the cutting-edge intersection of quantum computing and machine learning with "AI Foundations of Quantum Machine Learning." This comprehensive guide invites readers into the exciting world where the realms of artificial intelligence (AI) and quantum mechanics merge, setting the stage for a revolution in AI technologies. With the burgeoning interest in quantum computing's vast potential, this book serves as a beacon, illuminating the intricate concepts and groundbreaking promises of quantum machine learning. Contents Quantum Computing: An Introduction - Begin your journey with a primer on quantum computing, understanding the fundamental quantum mechanics that power advanced data processing. Fundamentals of Machine Learning - Lay the groundwork with an overview of machine learning principles, setting the stage for their quantum leap. Quantum Algorithms for Machine Learning - Discover the transformative potential of quantum algorithms, capable of processing large datasets with unprecedented speed and efficiency. Data Encoding in Quantum Systems - Explore the innovative techniques for encoding data into quantum systems, a crucial step for quantum machine learning. Quantum Machine Learning Models - Delve into the heart of quantum machine learning, examining models that harness quantum mechanics to enhance machine learning capabilities. Training Quantum Neural Networks - Unpack the methodologies for training quantum neural networks, a pioneering approach to AI development. Applications of Quantum Machine Learning - Witness the practical implications of quantum machine learning across various fields, from healthcare to environmental science. Challenges and the Future Landscape - Reflect on the hurdles facing quantum machine learning and envision the future of AI shaped by quantum advancements. Introduction "AI Foundations of Quantum Machine Learning" offers a compelling narrative on the symbiosis of quantum computing and machine learning. Through accessible language and vivid examples, it demystifies complex concepts and showcases the transformative power of quantum technologies in AI. Readers are taken on an enlightening journey, from the basic principles of quantum computing to the forefront of quantum machine learning models and their applications. This book is not merely an academic text; it is a roadmap to the future, encouraging readers to envision a world where AI is redefined by quantum phenomena. Ideal for students, academics, and tech enthusiasts alike, this book bridges the gap between theoretical quantum mechanics and practical machine learning applications. Whether you're looking to understand the basics or explore the future of technology, "AI Foundations of Quantum Machine Learning" is an indispensable resource for anyone eager to grasp the next wave of technological innovation.

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.

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.