Hamiltonian Meta-Learning

Precise calibration of quantum devices is necessary for reliable quantum information processing. Full characterization and tuning a quantum system without making any assumption require resources that scale exponentially with the system size. Here, we assume a model for the noisy evolution of a quantum system, and by using a machine learning technique known as meta-learning to train an optimizer that finds model parameters with less resources than other gradient-based optimization algorithms. The training of our algorithm is done efficiently on smaller systems. However, the learned optimizer is transferable to larger and different systems.