  Image classification is a major application domain for conventional deep
learning (DL). Quantum machine learning (QML) has the potential to
revolutionize image classification. In any typical DL-based image
classification, we use convolutional neural network (CNN) to extract features
from the image and multi-layer perceptron network (MLP) to create the actual
decision boundaries. On one hand, QML models can be useful in both of these
tasks. Convolution with parameterized quantum circuits (Quanvolution) can
extract rich features from the images. On the other hand, quantum neural
network (QNN) models can create complex decision boundaries. Therefore,
Quanvolution and QNN can be used to create an end-to-end QML model for image
classification. Alternatively, we can extract image features separately using
classical dimension reduction techniques such as, Principal Components Analysis
(PCA) or Convolutional Autoencoder (CAE) and use the extracted features to
train a QNN. We review two proposals on quantum-classical hybrid ML models for
image classification namely, Quanvolutional Neural Network and dimension
reduction using a classical algorithm followed by QNN. Particularly, we make a
case for trainable filters in Quanvolution and CAE-based feature extraction for
image datasets (instead of dimension reduction using linear transformations
such as, PCA). We discuss various design choices, potential opportunities, and
drawbacks of these models. We also release a Python-based framework to create
and explore these hybrid models with a variety of design choices.
