  Image classification, a pivotal task in multiple industries, faces
computational challenges due to the burgeoning volume of visual data. This
research addresses these challenges by introducing two quantum machine learning
models that leverage the principles of quantum mechanics for effective
computations. Our first model, a hybrid quantum neural network with parallel
quantum circuits, enables the execution of computations even in the noisy
intermediate-scale quantum era, where circuits with a large number of qubits
are currently infeasible. This model demonstrated a record-breaking
classification accuracy of 99.21% on the full MNIST dataset, surpassing the
performance of known quantum-classical models, while having eight times fewer
parameters than its classical counterpart. Also, the results of testing this
hybrid model on a Medical MNIST (classification accuracy over 99%), and on
CIFAR-10 (classification accuracy over 82%), can serve as evidence of the
generalizability of the model and highlights the efficiency of quantum layers
in distinguishing common features of input data. Our second model introduces a
hybrid quantum neural network with a Quanvolutional layer, reducing image
resolution via a convolution process. The model matches the performance of its
classical counterpart, having four times fewer trainable parameters, and
outperforms a classical model with equal weight parameters. These models
represent advancements in quantum machine learning research and illuminate the
path towards more accurate image classification systems.
