Quantum Machine Learning

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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• A física quântica (mecânica quântica e teoria quântica de campos)
• Teoria matemática da computação
• Aprendizagem mecânica
• Física Matemática
• Modelação matemática

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