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Titlebook: Quantum Machine Learning: An Applied Approach; The Theory and Appli Santanu Ganguly Book 2021 Santanu Ganguly 2021 Quantum Mechanics.machin

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發(fā)表于 2025-3-21 19:16:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Quantum Machine Learning: An Applied Approach
副標(biāo)題The Theory and Appli
編輯Santanu Ganguly
視頻videohttp://file.papertrans.cn/782/781272/781272.mp4
概述The first book related to hands-on aspects of quantum machine learning.Optimized for self-study without jargon and centered on easy reading.Code examples utilizing open source libraries and languages
圖書封面Titlebook: Quantum Machine Learning: An Applied Approach; The Theory and Appli Santanu Ganguly Book 2021 Santanu Ganguly 2021 Quantum Mechanics.machin
描述.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 v
出版日期Book 2021
關(guān)鍵詞Quantum Mechanics; machine learning; quantum computing; artificial intelligence; Grover’s search algorit
版次1
doihttps://doi.org/10.1007/978-1-4842-7098-1
isbn_softcover978-1-4842-7097-4
isbn_ebook978-1-4842-7098-1
copyrightSantanu Ganguly 2021
The information of publication is updating

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Rise of the Quantum Machines: Fundamentals,ways been a champion of thoughts leading toward computers leveraging laws of quantum mechanics when he stated, “There’s plenty of room at the bottom” and, “Nature isn’t classical. If you want to make a simulation of nature, you’d better make it quantum mechanical,” which means to properly simulate quantum systems, you must use a quantum computer.
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Deep Quantum Learning,ted annealing. Physics also offers the ., which, in its quantum form, is .. Hence, physical machines can solve the mathematical problem of optimization, including constraints—a property that can be extended to using quantum systems to treat classical and quantum data.
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