  Image classification is an important task in various machine learning
applications. In recent years, a number of classification methods based on
quantum machine learning and different quantum image encoding techniques have
been proposed. In this paper, we study the effect of three different quantum
image encoding approaches on the performance of a convolution-inspired hybrid
quantum-classical image classification algorithm called quanvolutional neural
network (QNN). We furthermore examine the effect of variational - i.e.
trainable - quantum circuits on the classification results. Our experiments
indicate that some image encodings are better suited for variational circuits.
However, our experiments show as well that there is not one best image
encoding, but that the choice of the encoding depends on the specific
constraints of the application.
