Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data

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.

Hybrid Quantum Temporal Convolutional Networks

Quantum machine learning models for sequential data face scalability challenges with complex multivariate signals. We introduce the Hybrid Quantum Temporal Convolutional Network (HQTCN), which combines classical temporal windowing with a quantum convolutional neural network core. By applying a shared quantum circuit across temporal windows, HQTCN captures long-range dependencies while achieving significant parameter reduction. Evaluated on synthetic NARMA sequences and high-dimensional EEG time-series, HQTCN performs competitively with classical baselines on univariate data and outperforms all baselines on multivariate tasks. The model demonstrates particular strength under data-limited conditions, maintaining high performance with substantially fewer parameters than conventional approaches. These results establish HQTCN as a parameter-efficient approach for multivariate time-series analysis.

Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles

Quantum Machine Learning (QML) holds significant promise for solving computational challenges across diverse domains. However, its practical deployment is constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, including noise, limited scalability, and trainability issues in variational quantum circuits (VQCs). We introduce the multi-chip ensemble VQC framework, which partitions high-dimensional computations across smaller quantum chips to enhance scalability, trainability, and noise resilience. We show that this approach mitigates barren plateaus, reduces quantum error bias and variance, and maintains robust generalization through controlled entanglement. Designed to align with current and emerging quantum hardware, the framework demonstrates strong potential for enabling scalable QML on near-term devices, as validated by experiments on standard benchmark datasets (MNIST, FashionMNIST, CIFAR-10) and real world dataset (PhysioNet EEG).