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Quantum machine learning = thinking and exploration in neural network models for quantum science and quantum computing /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantum machine learning/ by Claudio Conti.
其他題名:	thinking and exploration in neural network models for quantum science and quantum computing /
作者:	Conti, Claudio.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xxiii, 378 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1: Quantum mechanics and data-driven physics -- Chapter 2: Kernelizing quantum mechanics -- Chapter 3: Qubit maps -- Chapter 4: One qubit transverse-field Ising model and variational quantum algorithms -- Chapter 5: Two-qubit transverse-field Ising model and entanglement -- Chapter 6: Variational Algorithms, Quantum Approximation Optimization and Neural Network Quantum States with two-qubits -- Chapter 7: Phase space representation -- Chapter 8: States as a neural networks and gates as pullbacks -- Chapter 9: Quantum reservoir computing -- Chapter 10: Squeezing, beam splitters, and detection -- Chapter 11: Uncertainties and entanglement -- Chapter 12: Gaussian boson sampling -- Chapter 13: Variational circuits for quantum solitons.
Contained By:	Springer Nature eBook
標題:	Quantum theory. -
電子資源:	https://doi.org/10.1007/978-3-031-44226-1
ISBN:	9783031442261

Quantum machine learning = thinking and exploration in neural network models for quantum science and quantum computing /
Conti, Claudio.

Quantum machine learningthinking and exploration in neural network models for quantum science and quantum computing /[electronic resource] :by Claudio Conti. - Cham :Springer International Publishing :2024. - xxiii, 378 p. :ill. (some col.), digital ;24 cm. - Quantum science and technology,2364-9062. - Quantum science and technology..

Chapter 1: Quantum mechanics and data-driven physics -- Chapter 2: Kernelizing quantum mechanics -- Chapter 3: Qubit maps -- Chapter 4: One qubit transverse-field Ising model and variational quantum algorithms -- Chapter 5: Two-qubit transverse-field Ising model and entanglement -- Chapter 6: Variational Algorithms, Quantum Approximation Optimization and Neural Network Quantum States with two-qubits -- Chapter 7: Phase space representation -- Chapter 8: States as a neural networks and gates as pullbacks -- Chapter 9: Quantum reservoir computing -- Chapter 10: Squeezing, beam splitters, and detection -- Chapter 11: Uncertainties and entanglement -- Chapter 12: Gaussian boson sampling -- Chapter 13: Variational circuits for quantum solitons.

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.

ISBN: 9783031442261

Standard No.: 10.1007/978-3-031-44226-1doiSubjects--Topical Terms:

516552
Quantum theory.

LC Class. No.: QC174.12

Dewey Class. No.: 530.12

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520 $a 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.
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