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100 1 _aConti, Claudio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_968550
245 1 0 _aQuantum Machine Learning
_h[electronic resource] :
_bThinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing /
_cby Claudio Conti.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXXIII, 378 p. 109 illus., 66 illus. in color.
_bonline resource.
336 _atext
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337 _acomputer
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338 _aonline resource
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490 1 _aQuantum Science and Technology,
_x2364-9062
505 0 _aChapter 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.
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 0 _aQuantum physics.
_966608
650 0 _aMachine learning.
_955059
650 0 _aQuantum computers.
_965955
650 0 _aQuantum computing.
_964754
650 0 _aMathematical physics.
650 0 _aComputer simulation.
650 0 _aNeural networks (Computer science) .
_964231
650 1 4 _aQuantum Physics.
_966608
650 2 4 _aMachine Learning.
_955059
650 2 4 _aQuantum Computing.
_964754
650 2 4 _aQuantum Information.
_966611
650 2 4 _aComputational Physics and Simulations.
_965564
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_965860
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031442254
776 0 8 _iPrinted edition:
_z9783031442278
776 0 8 _iPrinted edition:
_z9783031442285
830 0 _aQuantum Science and Technology,
_x2364-9062
_968551
856 4 0 _uhttps://doi.org/10.1007/978-3-031-44226-1
912 _aZDB-2-PHA
912 _aZDB-2-SXP
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