  Quantum Machine Learning (QML) has come into the limelight due to the
exceptional computational abilities of quantum computers. With the promises of
near error-free quantum computers in the not-so-distant future, it is important
that the effect of multi-qubit interactions on quantum neural networks is
studied extensively. This paper introduces a Quantum Convolutional Network with
novel Interaction layers exploiting three-qubit interactions, while studying
the network's expressibility and entangling capability, for classifying both
image and one-dimensional data. The proposed approach is tested on three
publicly available datasets namely MNIST, Fashion MNIST, and Iris datasets,
flexible in performing binary and multiclass classifications, and is found to
supersede the performance of existing state-of-the-art methods.
