  Machine learning using quantum convolutional neural networks (QCNNs) has
demonstrated success in both quantum and classical data classification. In
previous studies, QCNNs attained a higher classification accuracy than their
classical counterparts under the same training conditions in the few-parameter
regime. However, the general performance of large-scale quantum models is
difficult to examine because of the limited size of quantum circuits, which can
be reliably implemented in the near future. We propose transfer learning as an
effective strategy for utilizing small QCNNs in the noisy intermediate-scale
quantum era to the full extent. In the classical-to-quantum transfer learning
framework, a QCNN can solve complex classification problems without requiring a
large-scale quantum circuit by utilizing a pre-trained classical convolutional
neural network (CNN). We perform numerical simulations of QCNN models with
various sets of quantum convolution and pooling operations for MNIST data
classification under transfer learning, in which a classical CNN is trained
with Fashion-MNIST data. The results show that transfer learning from classical
to quantum CNN performs considerably better than purely classical transfer
learning models under similar training conditions.
