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Principles of quantum artificial intelligence [electronic resource] : quantum problem solving and machine learning / by Andreas Wichert.

By: Wichert, Andrzej.
Material type: materialTypeLabelBookPublisher: Singapore : World Scientific, 2020Edition: 2nd ed.Description: 1 online resource (xviii, 478 p.).ISBN: 9789811224317; z789811224300.Subject(s): Quantum computers | Artificial intelligenceGenre/Form: Electronic books.DDC classification: 004.1 Online resources: Access to full text is restricted to subscribers.
Contents:
Introduction -- Computation -- Problem solving -- Information -- Reversible algorithms -- Probability -- Introduction to quantum physics -- Computation with qubits -- Periodicity -- Search -- Quantum problem-solving -- Grover's algorithm and the input problem -- Statistical machine learning -- Linear-algebra based quantum machine learning -- Stochastic methods -- Adiabatic quantum computation and quantum annealing -- Quantum cognition -- Quantum like-evolution -- Quantum computation and the multiverse -- Conclusion.
Summary: "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"--Publisher's website.
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Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

"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"--Publisher's website.

Introduction -- Computation -- Problem solving -- Information -- Reversible algorithms -- Probability -- Introduction to quantum physics -- Computation with qubits -- Periodicity -- Search -- Quantum problem-solving -- Grover's algorithm and the input problem -- Statistical machine learning -- Linear-algebra based quantum machine learning -- Stochastic methods -- Adiabatic quantum computation and quantum annealing -- Quantum cognition -- Quantum like-evolution -- Quantum computation and the multiverse -- Conclusion.

Includes bibliographical references and index.

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