  With the rapid advance of quantum machine learning, several proposals for the
quantum-analogue of convolutional neural network (CNN) have emerged. In this
work, we benchmark fully parameterized quantum convolutional neural networks
(QCNNs) for classical data classification. In particular, we propose a quantum
neural network model inspired by CNN that only uses two-qubit interactions
throughout the entire algorithm. We investigate the performance of various QCNN
models differentiated by structures of parameterized quantum circuits, quantum
data encoding methods, classical data pre-processing methods, cost functions
and optimizers on MNIST and Fashion MNIST datasets. In most instances, QCNN
achieved excellent classification accuracy despite having a small number of
free parameters. The QCNN models performed noticeably better than CNN models
under the similar training conditions. Since the QCNN algorithm presented in
this work utilizes fully parameterized and shallow-depth quantum circuits, it
is suitable for Noisy Intermediate-Scale Quantum (NISQ) devices.
