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

ArXi:2603.27269v1 Announce Type: cross Foundation models have recently improved electrocardiogram (ECG) representation learning, but their deployment can be limited by computational cost and latency constraints. In this work, we fine-tune ECGFounder as a high-capacity teacher for binary ECG classification on PTB-XL and the MIT-BIH Arrhythmia Database, and investigate whether knowledge distillation can transfer its predictive behavior to compact students.