£90.85

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

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£91 today · all-time low £88 (Jun 2025) · usually the usual

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£102.70 £86.86 £90.32 £93.77 £97.23 £100.68 £104.14 01 November 2024 17 February 2025 05 June 2025 21 September 2025 08 January 2026

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3 days 8 days 212 days 190 days · current 2 days 18 days 1 day 0 53 106 159 212 £88 £89 £90 £91 £94 £97 £103 Days at Price

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Price range: £88 - £103

Price levels: 7 different prices over 434 days

Description

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.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
03 January 2024
Listed Since
15 August 2023

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