量子模块压缩特征提取，提升混合神经网络性能
QuFeX: Quantum feature extraction module for hybrid quantum-classical deep neural networks
- 设计量子特征提取模块，降低并行计算量
- 在医学影像分割任务中，性能优于传统U-Net
- 适合需要高精度特征提取的混合量子-经典场景
我们提出量子特征提取（QuFeX）模块，一种新型量子机器学习组件。该模块可在低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评估次数。其设计支持无缝集成到深层经典神经网络中，特别适用于混合量子-经典模型。作为应用，我们构建了基于QuFeX的混合架构Qu-Net，将该模块置于U-Net的瓶颈层。U-Net广泛用于医学影像与自动驾驶中的图像分割任务。数值分析表明，Qu-Net相比基准U-Net在分割性能上表现更优。这些结果凸显了利用混合计算范式增强深度神经网络的潜力，为需精确特征提取的实际应用提供了稳健框架。
原文摘要 · Abstract (English)
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.
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