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

📰 ArXiv cs.AI

Q-DIVER integrates quantum transfer learning and differentiable quantum architecture search for EEG data classification

advanced Published 31 Mar 2026

Action Steps

Pretrain a large-scale EEG encoder using a dataset like PhysioNet Motor
Employ Differentiable Quantum Architecture Search to discover optimal quantum circuit topologies
Fine-tune the quantum classifier using the pretrained EEG encoder and discovered circuit topologies
Evaluate the performance of Q-DIVER on EEG data classification tasks

Who Needs to Know This

ML researchers and engineers working on quantum AI applications can benefit from Q-DIVER's hybrid framework, which enables autonomous discovery of task-optimal circuit topologies

Key Insight

💡 Q-DIVER's hybrid framework enables autonomous discovery of task-optimal quantum circuit topologies for EEG data classification

Key Takeaways

Q-DIVER integrates quantum transfer learning and differentiable quantum architecture search for EEG data classification

Full Article

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

Abstract:
arXiv:2603.28122v1 Announce Type: cross 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 I

Read full paper → ← Back to Reads