Quantum convolutional neural networks for multiclass image classification

The quantum convolutional neural networks (QCNNs) are emerging as a promising solution for image classification problems on near-term quantum devices. While QCNNs have shown encouraging results in binary classification tasks, their effectiveness in more complex multiclass classification tasks remains to be fully understood. In this study, we propose three distinct QCNN architectures, each inspired by the configuration of two-qubit entangling blocks available in quantum hardware. These QCNNs are designed based on different sliding modes of quantum filters. We investigate the impact of quantum filter structure, filter arrangement and parameter sharing among filters on the performance of QCNNs in multiclass classification. Our findings indicate that the specific structure of the quantum filter significantly affects the models’ performance. Moreover, we observed that unsharring parameters and more complex filter arrangements can significantly enhance the performance of QCNNs. These results contribute to the development of powerful quantum classifiers for multiclass image classification.