Quantum Many-Body Dynamics: Artificial Neural Network Approach

This lecture presents a paper on simulating quantum many-body dynamics using artificial neural networks to overcome the computational challenges posed by the exponential size of the Hilbert space. The paper introduces a novel scheme to stabilize the solutions and enhance the technical details of the neural network. It discusses the optimization objective, the regularization scheme based on signal-to-noise ratio, and the application of the method to the transverse Ising model on a square lattice. The results show successful simulation of quantum dynamics, entanglement development, and comparison with state-of-the-art techniques. The lecture highlights the advantages of the artificial neural network approach in terms of computational cost and system size scalability, while addressing remaining instabilities.