QuFeX: Quantum feature extraction module for hybrid quantum-classical deep neural networks
This work addresses computational bottlenecks in hybrid quantum-classical deep neural networks for applications like medical imaging and autonomous driving, though it appears incremental as it builds on existing U-Net architectures.
The authors tackled the problem of high computational cost in quantum convolutional neural networks by introducing QuFeX, a quantum feature extraction module that reduces the number of parallel evaluations, and demonstrated its application in Qu-Net, which achieved superior segmentation performance compared to a U-Net baseline.
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.