000			02598 a2200229 4500
003			OSt
005			20250728123659.0
008			250724b |||||||| |||| 00| 0 eng d
020			_a9783031442254
082			_a006.3843 _bC767q
100			_aConti, Claudio
245			_aQuantum machine learning _b thinking and exploration in neural network models for quantum science and quantum computing _cClaudio Conti
260			_bSpringer _c2024 _aSwitzerland
300			_axxiii, 378p
440			_aQuantum science and technology
490			_a / edited by Raymond Laflamme
520			_aThis 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			_aMachine learning
650			_aQuantum computing
650			_aQuantum theory
942			_cBK
999			_c567558 _d567558