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.Contents: IntroductionComputationProblem SolvingInformationReversible AlgorithmsProbabilityIntroduction to Quantum PhysicsComputation with QubitsPeriodicitySearchQuantum Problem-SolvingGrover's Algorithm and the Input ProblemStatistical Machine LearningLinear-Algebra Based Quantum Machine LearningStochastic MethodsAdiabatic Quantum Computation and Quantum AnnealingQuantum CognitionQuantum like-EvolutionQuantum Computation and the MultiverseConclusion
Readership: Professionals, researchers, academics, and graduate students in databases, artificial intelligence, pattern recognition and neural networks. Quantum Computing;Quantum Theory;AI;Machine Learning;Quantum Machine Learning;Quantum Cognition;Multiverse00
