  This article explores search strategies for the design of parameterized
quantum circuits. We propose several optimization approaches including random
search plus survival of the fittest, reinforcement learning both with classical
and hybrid quantum classical controllers and Bayesian optimization as decision
makers to design a quantum circuit in an automated way for a specific task such
as multi-labeled classification over a dataset. We introduce nontrivial circuit
architectures that are arduous to be hand-designed and efficient in terms of
trainability. In addition, we introduce reuploading of initial data into
quantum circuits as an option to find more general designs. We numerically show
that some of the suggested architectures for the Iris dataset accomplish better
results compared to the established parameterized quantum circuit designs in
the literature. In addition, we investigate the trainability of these
structures on the unseen dataset Glass. We report meaningful advantages over
the benchmarks for the classification of the Glass dataset which supports the
fact that the suggested designs are inherently more trainable.
