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

Conti, Claudio

 
9783031442254: Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing (Quantum Science and Technology)
Hardcover
ISBN 10: 3031442253 ISBN 13: 9783031442254
Verlag: Springer, 2024

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Claudio Conti is an associate professor at the Department of Physics of the University Sapienza of Rome. He authored over 250 articles in many fields, such as quantum physics, photonics, nonlinear science, biophysics, and complexity. His activity includes experiments and theory, such as the first observation of replica symmetry breaking mentioned in the scientific background of the Nobel prize in physics in 2021, the investigation of neuromorphic computing by quantum fluids, and the optical acceleration of natural language processing. Claudio Conti coordinates an experimental and theoretical group in Rome exploring the frontiers of artificial intelligence and physics.

Von der hinteren Coverseite

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Springer
Erscheinungsdatum
2024
Sprache
Englisch
ISBN 10 
3031442253
ISBN 13 
9783031442254
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
401
Kontakt zum Hersteller
preigu GmbH & Co. KG
mail@preigu.de

Lengericher Landstr. 19
Osnabrück
Niedersachsen
49078
Deutschland

Weitere beliebte Ausgaben desselben Titels

9783031442285: Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing (Quantum Science and Technology)

Vorgestellte Ausgabe

ISBN 10:  3031442288 ISBN 13:  9783031442285
Verlag: Springer, 2025
Softcover