  Barren plateaus are a notorious problem in the optimization of variational
quantum algorithms and pose a critical obstacle in the quest for more efficient
quantum machine learning algorithms. Many potential reasons for barren plateaus
have been identified but few solutions have been proposed to avoid them in
practice. Existing solutions are mainly focused on the initialization of
unitary gate parameters without taking into account the changes induced by
input data. In this paper, we propose an alternative strategy which initializes
the parameters of a unitary gate by drawing from a beta distribution. The
hyperparameters of the beta distribution are estimated from the data. To
further prevent barren plateau during training we add a novel perturbation at
every gradient descent step. Taking these ideas together, we empirically show
that our proposed framework significantly reduces the possibility of a complex
quantum neural network getting stuck in a barren plateau.
