Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing (Quantum Science and Technology)
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Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing (Quantum Science and Technology)
This book opens rich possibilities in terms of computation, efficient hardware, and scalability.
Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing (Quantum Science and Technology)
товар №: 243033645

Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing (Quantum Science and Technology)

85% of respondents would recommend this to a friend

товар №: 243033645

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This book opens rich possibilities in terms of computation, efficient hardware, and scalability.
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What Stands Out

Interdisciplinary Approach
Bridges gaps between quantum science and machine learning, offering a comprehensive understanding for researchers across fields, enhancing collaborative opportunities in groundbreaking technology.
Advanced Neural Models
Introduces cutting-edge neural network models specialized for quantum systems, significantly improving data processing efficiency and accuracy compared to traditional machine learning techniques.
Practical Applications
Provides real-world examples and case studies, enabling users to apply theoretical concepts effectively in quantum computing projects, thereby increasing their practical knowledge and skills.

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Shop Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing (Quantum Science and Technology) online at a best price in Kyrgyzstan. 3031442253
  • This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning.
Publisher Springer
Publication date January 3, 2024
Edition 2024th
Language English
Print length 401 pages
ISBN-10 3031442253
ISBN-13 978-3031442254
Item Weight 1.63 pounds (740 grams)
Dimensions 6.14 x 0.94 x 9.21 inches (15.6 x 2.4 x 23.4 cm)

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  • вопрос: What programming languages are used in the book?

    отвечать: The book features open source examples in Python and TensorFlow, along with routines for MATLAB and Mathematica.
  • вопрос: Who is the target audience for this book?

    отвечать: It is targeted at graduate students and researchers looking to deepen their understanding of quantum mechanics and machine learning.
  • вопрос: How does this book contribute to quantum computing?

    отвечать: It presents neural network models that enhance computation capabilities, efficient hardware, and scalability in quantum systems.

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KGS 12806

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Особенности и преимущества

  • Merges quantum mechanics with machine learning.
  • Demonstrates neural network models for quantum physics.
  • Optimizes applications and fine-tunes theories.
  • Includes programming tools and concepts in Python and TensorFlow.
  • Covers non-classical state representations and quantum computing platforms.
  • Essential for graduate students and researchers in the field.

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