QDCNN: Quantum Dilated Convolutional Neural Network
This work addresses the challenge of enhancing quantum machine learning models for image recognition, but it is incremental as it builds on existing hybrid quantum-classical neural networks.
The authors tackled the problem of improving quantum convolutional neural networks by proposing QDCNN, a hybrid quantum-classical algorithm that extends dilated convolution to quantum contexts, resulting in better accuracy and computational efficiency on MNIST and Fashion-MNIST datasets compared to existing QCNNs.
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively explored for the purpose of improving the performance of classical neural networks. In this paper, we propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs). Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost. We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and computation efficiency compared to existing quantum convolutional neural networks (QCNNs).