  Efficient characterization of quantum devices is a significant challenge
critical for the development of large scale quantum computers. We consider an
experimentally motivated situation, in which we have a decent estimate of the
Hamiltonian, and its parameters need to be characterized and fine-tuned
frequently to combat drifting experimental variables. We use a machine learning
technique known as meta-learning to learn a more efficient optimizer for this
task. We consider training with the nearest-neighbor Ising model and study the
trained model's generalizability to other Hamiltonian models and larger system
sizes. We observe that the meta-optimizer outperforms other optimization
methods in average loss over test samples. This advantage follows from the
meta-optimizer being less likely to get stuck in local minima, which highly
skews the distribution of the final loss of the other optimizers. In general,
meta-learning decreases the number of calls to the experiment and reduces the
needed classical computational resources.
