Classical-to-quantum convolutional neural network transfer learning
This work addresses the problem of scaling quantum machine learning for researchers in the noisy intermediate-scale quantum era, though it is incremental as it builds on existing transfer learning and QCNN methods.
The authors tackled the challenge of limited quantum circuit size by proposing classical-to-quantum transfer learning, where a small quantum convolutional neural network (QCNN) leverages a pre-trained classical CNN to solve complex classification problems, achieving better performance than purely classical transfer learning models in MNIST data classification.
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.