Quantum Machine Learning

Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing

AvClaudio Conti

Inbunden, Engelska, 2024

Del i serien Quantum Science and Technology

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2024-01-03
Mått:155 x 235 x 28 mm
Vikt:770 g
Format:Inbunden
Språk:Engelska
Serie:Quantum Science and Technology
Antal sidor:378
Förlag:Springer International Publishing AG
ISBN:9783031442254

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Matematisk fysik inom Naturvetenskap och teknik
Kvantfysik inom Naturvetenskap och teknik

Mer om författaren

Claudio Conti is an associate professor at the Department of Physics of the University Sapienza of Rome. He authored over 250 articles in many fields, such as quantum physics, photonics, nonlinear science, biophysics, and complexity. His activity includes experiments and theory, such as the first observation of replica symmetry breaking mentioned in the scientific background of the Nobel prize in physics in 2021, the investigation of neuromorphic computing by quantum fluids, and the optical acceleration of natural language processing. Claudio Conti coordinates an experimental and theoretical group in Rome exploring the frontiers of artificial intelligence and physics.

Innehållsförteckning

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.