This paper explores the performance of quantum-classical hybrid networks in image classification tasks, focusing on the integration of quantum circuits as alternative feature extractors to traditional convolutional layers. Specifically, it investigates the use of quanvolutional layers, variational quantum circuits that leverage quantum entanglement and quantum gates, in comparison to classical layers. The study examines various quantum pooling techniques, including conditional entanglement gates, and their impact on classification accuracy across datasets with varying complexity. By experimenting with different pooling strategies, both parametrized and non-parametrized, this work assesses their influence on network performance and feature representation. Results indicate that quanvolutional layers in a hybrid network consistently outperform classical convolutional layers in terms of classification accuracy, particularly when applied to datasets with prominent features. Additionally, the findings suggest that quantum entanglement techniques, rather than rotation parameters, play a more significant role in enhancing performance. This paper concludes that quantum-classical hybrid networks offer a promising approach for feature extraction, although the optimal configuration of pooling methods depends on the characteristics of the data. Future research could further explore the interplay between quantum circuits and different pooling strategies for more effective feature representation.
Faier et al. (Tue,) studied this question.