東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantum machine learning/ by Claudio Conti.
Reminder of title:	thinking and exploration in neural network models for quantum science and quantum computing /
Author:	Conti, Claudio.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xxiii, 378 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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
Subject:	Quantum theory. -
Online resource:	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

Quantum machine learning = thinking and exploration in neural network models for quantum science and quantum computing /
LDR:03753nmm a2200337 a 4500 001 2368268
003 DE-He213
005 20231227173610.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031442261 $q (electronic bk.)
020 $a 9783031442254 $q (paper)
024 7 $a 10.1007/978-3-031-44226-1 $2 doi
035 $a 978-3-031-44226-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QC174.12
072 7 $a PHQ $2 bicssc
072 7 $a SCI057000 $2 bisacsh
072 7 $a PHQ $2 thema
082 0 4 $a 530.12 $2 23
090 $a QC174.12 $b .C762 2024
100 1 $a Conti, Claudio. $3 3712109
245 1 0 $a Quantum machine learning $h [electronic resource] : $b thinking and exploration in neural network models for quantum science and quantum computing / $c by Claudio Conti.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xxiii, 378 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Quantum science and technology, $x 2364-9062
505 0 $a 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.
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.
650 0 $a Quantum theory. $3 516552
650 0 $a Machine learning. $3 533906
650 0 $a Quantum computing. $3 2115803
650 1 4 $a Quantum Physics. $3 893952
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Quantum Information. $3 3594468
650 2 4 $a Computational Physics and Simulations. $3 3538874
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Quantum science and technology. $3 2054889
856 4 0 $u https://doi.org/10.1007/978-3-031-44226-1
950 $a Physics and Astronomy (SpringerNature-11651)