On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification
The research integrates quantum machine learning with classical deep learning for medical image analysis using a dual-branch feature extraction pipeline. It proposes Temperature-Scaled Hybrid Fusion (TSHF), an adaptive method that dynamically reweights classical and quantum features. This approach achieved state-of-the-art results on BreastMNIST, showing that combining both paradigms creates richer data context and superior classification performance.