05035nam a22006255i 4500 978-3-031-44226-1 DE-He213 20251030110900.0 cr nn 008mamaa 231227s2024 sz | s |||| 0|eng d 9783031442261 978-3-031-44226-1 10.1007/978-3-031-44226-1 doi QC173.96-174.52 PHQ bicssc SCI057000 bisacsh PHQ thema Conti, Claudio. author. aut http://id.loc.gov/vocabulary/relators/aut 68550 Quantum Machine Learning [electronic resource] : Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing / by Claudio Conti. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XXIII, 378 p. 109 illus., 66 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Quantum Science and Technology, 2364-9062 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. Quantum physics. 66608 Machine learning. 55059 Quantum computers. 65955 Quantum computing. 64754 Mathematical physics. Computer simulation. Neural networks (Computer science) . 64231 Quantum Physics. 66608 Machine Learning. 55059 Quantum Computing. 64754 Quantum Information. 66611 Computational Physics and Simulations. 65564 Mathematical Models of Cognitive Processes and Neural Networks. 65860 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031442254 Printed edition: 9783031442278 Printed edition: 9783031442285 Quantum Science and Technology, 2364-9062 68551 https://doi.org/10.1007/978-3-031-44226-1 ZDB-2-PHA ZDB-2-SXP 56901 56901