Neural Network Quantum State Tomography

This lecture covers the application of neural networks for quantum state tomography, focusing on highly-entangled quantum systems. It explains the use of artificial neural networks to approximate many-body wave functions, training schemes, and the reconstruction accuracy. The lecture also discusses the ML approach to quantum state tomography, the RBM models, and the overfitting phenomenon in powerful models. Furthermore, it explores the implementation of QST on W states and genuine many-body problems, including the comparison with quantum Monte Carlo simulations.