  Machine learning algorithms, both in their classical and quantum versions,
heavily rely on optimization algorithms based on gradients, such as gradient
descent and alike. The overall performance is dependent on the appearance of
local minima and barren plateaus, which slow-down calculations and lead to
non-optimal solutions. In practice, this results in dramatic computational and
energy costs for AI applications. In this paper we introduce a generic strategy
to accelerate and improve the overall performance of such methods, allowing to
alleviate the effect of barren plateaus and local minima. Our method is based
on coordinate transformations, somehow similar to variational rotations, adding
extra directions in parameter space that depend on the cost function itself,
and which allow to explore the configuration landscape more efficiently. The
validity of our method is benchmarked by boosting a number of quantum machine
learning algorithms, getting a very significant improvement in their
performance.
