  A quantum neural network (QNN) is interpreted today as any quantum circuit
with trainable continuous parameters. This work builds on previous works by the
authors and addresses QNN for image classification with Novel Enhanced Quantum
Representation of (NEQR) processed classical data where Principal component
analysis (PCA) and Projected Quantum Kernel features (PQK) were investigated
previously by the authors as a path to quantum advantage for the same classical
dataset. For each of these cases the Fashion-MNIST dataset was downscaled using
PCA to convert into quantum data where the classical NN easily outperformed the
QNN. However, we demonstrated quantum advantage by using PQK where quantum
models achieved more than ~90% accuracy surpassing their classical counterpart
on the same training dataset as in the first case. In this current work, we use
the same dataset fed into a QNN and compare that with performance of a
classical NN model. We built an NEQR model circuit to pre-process the same data
and feed the images into the QNN. Our results showed marginal improvements
(only about ~5.0%) where the QNN performance with NEQR exceeded the performance
of QNN without NEQR. We conclude that given the computational cost and the
massive circuit depth associated with running NEQR, the advantage offered by
this specific Quantum Image Processing (QIMP) algorithm is questionable at
least for classical image dataset. No actual quantum computing hardware
platform exists today that can support the circuit depth needed to run NEQR
even for the reduced image sizes of our toy classical dataset.
