Quantum Machine Learning

Quantum Machine Learning

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

Conti, Claudio

Springer International Publishing AG

01/2025

378

Mole

9783031442285

15 a 20 dias

Descrição não disponível.

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.

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data-driven quantum physics;neural networks for quantum mechanics;boson sampling;machine learning in quantum phase space;computational many-body physics;quantum reservoir computing;Gaussian boson sampling;programming of quantum computers;Tensorflow for quantum physics;neural networks in phase space