  The rapid development of quantum computers promises transformative impacts
across diverse fields of science and technology. Quantum neural networks
(QNNs), as a forefront application, hold substantial potential. Despite the
multitude of proposed models in the literature, persistent challenges, notably
the vanishing gradient (VG) and cost function concentration (CFC) problems,
impede their widespread success. In this study, we introduce a novel approach
to quantum neural network construction, specifically addressing the issues of
VG and CFC. Our methodology employs ensemble learning, advocating for the
simultaneous deployment of multiple quantum circuits with a depth equal to $1$,
a departure from the conventional use of a single quantum circuit with depth
$L$. We assess the efficacy of our proposed model through a comparative
analysis with a conventionally constructed QNN. The evaluation unfolds in the
context of a classification problem, yielding valuable insights into the
potential advantages of our innovative approach.
