KACQ-DCNN
Quantum-Classical Dual-Channel Neural Network for Heart Disease Detection
KACQ-DCNN is an uncertainty-aware, interpretable neural architecture combining Kolmogorov-Arnold Networks (KAN) with classical-quantum dual-channel processing for heart disease detection.
Key Contributions
- Dual-channel design: classical CNN branch + variational quantum circuit branch
- Kolmogorov-Arnold layer for enhanced interpretability over standard MLPs
- Uncertainty quantification via Monte Carlo dropout for reliable clinical predictions
- Published in Computers in Biology and Medicine (2025)