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.

翻译：利用量子卷积神经网络（QCNN）的机器学习在量子和经典数据分类中均展现出成功应用。先前研究表明，在少参数情况下，QCNN在相同训练条件下能达到比经典卷积神经网络更高的分类准确率。然而，由于近期可可靠实现的量子电路规模有限，大规模量子模型的通用性能难以检验。我们提出将迁移学习作为有效策略，在含噪中等规模量子时代充分发挥小型QCNN的潜力。在经典到量子迁移学习框架中，QCNN通过利用预训练的经典卷积神经网络（CNN），无需大型量子电路即可解决复杂分类问题。我们针对MNIST数据分类任务，在迁移学习框架下数值模拟了采用不同量子卷积与池化操作组合的QCNN模型，其中经典CNN使用Fashion-MNIST数据进行预训练。结果表明，在相似训练条件下，经典到量子CNN的迁移学习性能显著优于纯经典迁移学习模型。