On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification

TL;DR

This paper proposes a hybrid quantum-classical framework for breast cancer classification, combining diverse feature representations with novel fusion strategies, notably the Temperature-Scaled Hybrid Fusion, to enhance diagnostic accuracy.

Contribution

It introduces a new hybrid architecture with three fusion strategies, including a novel temperature-scaled method, to unify quantum and classical features for improved medical image analysis.

Findings

The TSHF strategy with a ResNet backbone and trainable quantum circuit achieved 87.82% accuracy.

The hybrid framework outperformed purely classical models in accuracy, F1-score, and AUC-ROC.

Empirical validation on BreastMNIST confirms the effectiveness of unifying quantum and classical features.

Abstract

The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, effectively unifying these distinct paradigms remains challenging due to common optimization asymmetries. In this paper, a novel hybrid quantum-classical architecture for breast cancer diagnosis based on a dual-branch feature-extraction pipeline is proposed. Our framework extracts and unifies complementary representations from classical models and quantum circuits, exploring both trainable and deterministic (non-trainable) quantum paradigms. To integrate these embeddings, three progressive feature fusion strategies are introduced: Static Hybrid Fusion (SHF) for offline extraction, Dynamic Hybrid Fusion (DHF) for end-to-end co-adaptation, and a novel Temperature-Scaled Hybrid Fusion (TSHF). The TSHF strategy incorporates a learnable scalar, inspired by multimodal learning, that dynamically balances hybrid gradient dynamics and resolves optimization bottlenecks. Empirical validation on the BreastMNIST dataset confirms our hypothesis that unifying diverse feature representations creates a richer data context. The TSHF strategy, specifically when pairing a ResNet backbone with a trainable quantum circuit, achieved a peak accuracy of 87.82%, F1-score of 91.77%, and an AUC-ROC of 89.08%, outperforming purely classical baselines. These results demonstrate that the proposed hybrid framework improves classification accuracy and threshold reliability, providing a stable, high-performance architecture for the clinical deployment of quantum-enhanced diagnostic tools.