  Quantum machine learning, focusing on quantum neural networks (QNNs), remains
a vastly uncharted field of study. Current QNN models primarily employ
variational circuits on an ansatz or a quantum feature map, often requiring
multiple entanglement layers. This methodology not only increases the
computational cost of the circuit beyond what is practical on near-term quantum
devices but also misleadingly labels these models as neural networks, given
their divergence from the structure of a typical feed-forward neural network
(FFNN). Moreover, the circuit depth and qubit needs of these models scale
poorly with the number of data features, resulting in an efficiency challenge
for real-world machine-learning tasks. We introduce a bona fide QNN model,
which seamlessly aligns with the versatility of a traditional FFNN in terms of
its adaptable intermediate layers and nodes, absent from intermediate
measurements such that our entire model is coherent. This model stands out with
its reduced circuit depth and number of requisite C-NOT gates to outperform
prevailing QNN models. Furthermore, the qubit count in our model remains
unaffected by the data's feature quantity. We test our proposed model on
various benchmarking datasets such as the diagnostic breast cancer (Wisconsin)
and credit card fraud detection datasets. We compare the outcomes of our model
with the existing QNN methods to showcase the advantageous efficacy of our
approach, even with a reduced requirement on quantum resources. Our model paves
the way for application of quantum neural networks to real relevant machine
learning problems.
