From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student

1. 1Fine-tune ECGFounder on PTB-XL and MIT-BIH Arrhythmia Database for binary ECG classification
2. 2Investigate knowledge distillation to transfer predictive behavior to compact students
3. 3Evaluate classical 1D student models for knowledge distillation
4. 4Compare performance of compact students with the teacher model

AI engineers and researchers on a team can benefit from this research as it explores the application of knowledge distillation in ECG classification, while data scientists can utilize the findings to improve model performance

💡 Knowledge distillation can effectively transfer predictive behavior from high-capacity teacher models to compact student models

🚀 Fine-tuning ECGFounder for ECG classification & exploring knowledge distillation to compact students