  Parameterized quantum circuits (PQCs) have emerged as a foundational element
in the development and applications of quantum algorithms. However, when
initialized with random parameter values, PQCs often exhibit barren plateaus
(BP). These plateaus, characterized by vanishing gradients with an increasing
number of qubits, hinder optimization in quantum algorithms. In this paper, we
analyze the impact of state-of-the-art parameter initialization strategies from
classical machine learning in random PQCs from the aspect of BP phenomenon. Our
investigation encompasses a spectrum of initialization techniques, including
random, Xavier (both normal and uniform variants), He, LeCun, and Orthogonal
methods. Empirical assessment reveals a pronounced reduction in variance decay
of gradients across all these methodologies compared to the randomly
initialized PQCs. Specifically, the Xavier initialization technique outperforms
the rest, showing a 62\% improvement in variance decay compared to the random
initialization. The He, Lecun, and orthogonal methods also display
improvements, with respective enhancements of 32\%, 28\%, and 26\%. This
compellingly suggests that the adoption of these existing initialization
techniques holds the potential to significantly amplify the training efficacy
of Quantum Neural Networks (QNNs), a subclass of PQCs. Demonstrating this
effect, we employ the identified techniques to train QNNs for learning the
identity function, effectively mitigating the adverse effects of BPs. The
training performance, ranked from the best to the worst, aligns with the
variance decay enhancement as outlined above. This paper underscores the role
of tailored parameter initialization in mitigating the BP problem and
eventually enhancing the training dynamics of QNNs.
