Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data
This work addresses parameter efficiency in quantum deep learning for high-dimensional biological signal processing, representing an incremental advance in hybrid quantum-classical methods.
The paper tackled the challenge of integrating quantum circuits into deep learning by proposing Q-DIVER, a hybrid framework that combines a pretrained EEG encoder with a differentiable quantum classifier, achieving test F1 of 63.49% on the PhysioNet Motor Imagery dataset while using 50x fewer parameters than classical methods.
Integrating quantum circuits into deep learning pipelines remains challenging due to heuristic design limitations. We propose Q-DIVER, a hybrid framework combining a large-scale pretrained EEG encoder (DIVER-1) with a differentiable quantum classifier. Unlike fixed-ansatz approaches, we employ Differentiable Quantum Architecture Search to autonomously discover task-optimal circuit topologies during end-to-end fine-tuning. On the PhysioNet Motor Imagery dataset, our quantum classifier achieves predictive performance comparable to classical multi-layer perceptrons (Test F1: 63.49\%) while using approximately \textbf{50$\times$ fewer task-specific head parameters} (2.10M vs. 105.02M). These results validate quantum transfer learning as a parameter-efficient strategy for high-dimensional biological signal processing.