We introduce Quantum Feature Extraction (QuFeX), a novel quantum machine learning module. The proposed module enables feature extraction in a reduced-dimensional space, significantly decreasing the number of parallel evaluations required in typical quantum convolutional neural network architectures. Its design allows seamless integration into deep classical neural networks, making it particularly suitable for hybrid quantum-classical models. As an application of QuFeX, we propose Qu-Net -- a hybrid architecture which integrates QuFeX at the bottleneck of a U-Net architecture. The latter is widely used for image segmentation tasks such as medical imaging and autonomous driving. Our numerical analysis indicates that the Qu-Net can achieve superior segmentation performance compared to a U-Net baseline. These results highlight the potential of QuFeX to enhance deep neural networks by leveraging hybrid computational paradigms, providing a path towards a robust framework for real-world applications requiring precise feature extraction.

翻译：本文提出量子特征提取（QuFeX）这一新型量子机器学习模块。该模块能在降维空间中实现特征提取，显著减少典型量子卷积神经网络架构所需的并行计算量。其设计支持与经典深度神经网络的无缝集成，尤其适用于混合量子-经典模型。作为QuFeX的应用案例，我们提出Qu-Net——一种将QuFeX集成于U-Net架构瓶颈层的混合架构。U-Net广泛应用于医学影像与自动驾驶等图像分割任务。数值分析表明，相较于基准U-Net模型，Qu-Net能实现更优的分割性能。这些结果凸显了QuFeX通过融合混合计算范式增强深度神经网络的潜力，为需要精确特征提取的实际应用提供了通向稳健框架的技术路径。