  Variational quantum algorithms, a class of quantum heuristics, are promising
candidates for the demonstration of useful quantum computation. Finding the
best way to amplify the performance of these methods on hardware is an
important task. Here, we evaluate the optimization of quantum heuristics with
an existing class of techniques called `meta-learners'. We compare the
performance of a meta-learner to Bayesian optimization, evolutionary
strategies, L-BFGS-B and Nelder-Mead approaches, for two quantum heuristics
(quantum alternating operator ansatz and variational quantum eigensolver), on
three problems, in three simulation environments. We show that the meta-learner
comes near to the global optima more frequently than all other optimizers we
tested in a noisy parameter setting environment. We also find that the
meta-learner is generally more resistant to noise, for example seeing a smaller
reduction in performance in Noisy and Sampling environments and performs better
on average by a `gain' metric than its closest comparable competitor L-BFGS-B.
These results are an important indication that meta-learning and associated
machine learning methods will be integral to the useful application of noisy
near-term quantum computers.
