Quantum Machine Learning Applied by Ganguly Santanu (25 results)

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Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

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Paperback or Softback. Condition: New. Quantum Machine Learning: An Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry. Book.

Condition: As New. Unread book in perfect condition.

Condition: New.

Paperback. Condition: New. 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 LearnUnderstand and explore quantum computing and quantum machine learning, and their application in science and industryExplore variousdata training models utilizing quantum machine learning algorithms and Python librariesGet hands-on and familiar with applied quantum computing, including freely available cloud-based accessBe familiar with techniques for training and scaling quantum neural networksGain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep diveWho This Book Is ForData scientists, machine learning professionals, and researchers.…

Condition: New.

Condition: New.

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condition: New.

Paperback. Condition: New. 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 LearnUnderstand and explore quantum computing and quantum machine learning, and their application in science and industryExplore variousdata training models utilizing quantum machine learning algorithms and Python librariesGet hands-on and familiar with applied quantum computing, including freely available cloud-based accessBe familiar with techniques for training and scaling quantum neural networksGain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep diveWho This Book Is ForData scientists, machine learning professionals, and researchers.…

Condition: New. 2021. 1st ed. paperback. . . . . .

Condition: As New. Unread book in perfect condition.

Condition: New.

Paperback. Condition: New.

Condition: New. 2021. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condition: Brand New. 570 pages. 9.25x6.10x1.18 inches. In Stock.

Condition: New. In English.

Paperback. Condition: New. 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 LearnUnderstand and explore quantum computing and quantum machine learning, and their application in science and industryExplore variousdata training models utilizing quantum machine learning algorithms and Python librariesGet hands-on and familiar with applied quantum computing, including freely available cloud-based accessBe familiar with techniques for training and scaling quantum neural networksGain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep diveWho This Book Is ForData scientists, machine learning professionals, and researchers.…

Taschenbuch. Condition: Neu. Quantum Machine Learning: An Applied Approach | The Theory and Application of Quantum Machine Learning in Science and Industry | Santanu Ganguly | Taschenbuch | xix | Englisch | 2021 | Apress | EAN 9781484270974 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condition: New. 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 LearnUnderstand and explore quantum computing and quantum machine learning, and their application in science and industryExplore variousdata training models utilizing quantum machine learning algorithms and Python librariesGet hands-on and familiar with applied quantum computing, including freely available cloud-based accessBe familiar with techniques for training and scaling quantum neural networksGain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep diveWho This Book Is ForData scientists, machine learning professionals, and researchers.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 variousdata 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 ForData scientists, machine learning professionals, and researchers 551 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. intermediate-Advanced user level|The first book related to hands-on aspects of quantum machine learningOptimized for self-study without jargon and centered on easy readingCode examples utilizing open source libraries and languages are avail.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 variousdata 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 ForData scientists, machine learning professionals, and researchers.…