Quantum Dynamic

Quantum dynamics research focuses on efficiently simulating and understanding the time evolution of quantum systems, a task hampered by the exponential scaling of computational resources with system size. Current efforts concentrate on developing and applying machine learning techniques, including neural networks (e.g., Fourier Neural Operators, normalizing flows), variational quantum algorithms, and tensor networks, to learn and predict quantum dynamics from data or to optimize quantum control strategies. These advancements are crucial for advancing quantum computation, materials science, and other fields by enabling the simulation of larger and more complex quantum systems than previously possible.