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

Classification of NEQR Processed Classical Images using Quantum Neural Networks (QNN)