Abstract
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.
Abstract (translated)
将量子电路集成到深度学习流程中仍受限于启发式设计的局限性。我们提出Q-DIVER混合框架，该框架结合大规模预训练脑电图编码器（DIVER-1）与可微分量子分类器。不同于固定架构方法，我们采用可微分量子架构搜索，在端到端微调过程中自主发现任务最优电路拓扑结构。在PhysioNet运动想象数据集上，我们的量子分类器实现了与经典多层感知器相当的预测性能（测试F1值：63.49%），同时任务特定头部参数使用量减少约\textbf{50倍}（2.10M vs. 105.02M）。这些结果验证了量子迁移学习作为高维生物信号处理中参数高效策略的可行性。
URL
https://arxiv.org/abs/2603.28122
