Quantum Machine Learning
- Indbinding:
- Hardback
- Sideantal:
- 404
- Udgivet:
- 3. januar 2024
- Udgave:
- 24001
- Størrelse:
- 160x28x241 mm.
- Vægt:
- 770 g.
- 8-11 hverdage.
- 9. december 2024
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
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Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Quantum Machine Learning
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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Bogen Quantum Machine Learning findes i følgende kategorier:
- Business og læring > Computer og IT
- Business og læring > Videnskab
- Matematik og naturvidenskab > Matematik > Anvendt matematik > Matematisk modellering
- Matematik og naturvidenskab > Fysik > Kvantefysik (kvantemekanik og kvantefeltteori)
- Matematik og naturvidenskab > Fysik > Matematisk fysik
- Databehandling og informationsteknologi > Informatik > Matematisk datateori
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Machine learning
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