  When compared to fault-tolerant quantum computational strategies, variational
quantum algorithms stand as one of the candidates with the potential of
achieving quantum advantage for real-world applications in the near term.
However, the optimization of the circuit parameters remains arduous and is
impeded by many obstacles such as the presence of barren plateaus, many local
minima in the optimization landscape, and limited quantum resources. A
non-random initialization of the parameters seems to be key to the success of
the parametrized quantum circuits (PQC) training. Drawing and extending ideas
from the field of meta-learning, we address this parameter initialization task
with the help of machine learning and propose FLIP: a FLexible Initializer for
arbitrarily-sized Parametrized quantum circuits. FLIP can be applied to any
family of PQCs, and instead of relying on a generic set of initial parameters,
it is tailored to learn the structure of successful parameters from a family of
related problems which are used as the training set. The flexibility advocated
to FLIP hinges in the possibility of predicting the initialization of
parameters in quantum circuits with a larger number of parameters from those
used in the training phase. This is a critical feature lacking in other
meta-learning parameter initializing strategies proposed to date. We illustrate
the advantage of using FLIP in three scenarios: a family of problems with
proven barren plateaus, PQC training to solve max-cut problem instances, and
PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
