  In this work, we address the problem of automating quantum variational
machine learning. We develop a multi-locality parallelizable search algorithm,
called MUSE, to find the initial points and the sets of parameters that achieve
the best performance for quantum variational circuit learning. Simulations with
five real-world classification datasets indicate that on average, MUSE improves
the detection accuracy of quantum variational classifiers 2.3 times with
respect to the observed lowest scores. Moreover, when applied to two real-world
regression datasets, MUSE improves the quality of the predictions from negative
coefficients of determination to positive ones. Furthermore, the classification
and regression scores of the quantum variational models trained with MUSE are
on par with the classical counterparts.
