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

• URL: http://arxiv.org/abs/2603.27269v1
• Date: Sat, 28 Mar 2026 13:36:45 GMT
• Title: From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student
• Abstract summary: We fine-tune ECGFounder as a high-capacity teacher for binary ECG classification on PTB-XL and the MIT-BIH Arrhythmia Database.<n>We investigate whether knowledge distillation can transfer its predictive behavior to compact students.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We evaluate two classical 1D students (ResNet-1D and a lightweight CNN-1D) and a quantum-ready pipeline that combines a convolutional autoencoder, which compresses 256-sample ECG windows into a low-dimensional latent representation, with a 6-qubit variational quantum circuit implemented in Qiskit and executed in a simulated backend. Across both datasets, the teacher provides the strongest overall performance, while distillation yields competitive students under a considerable reduction in trainable parameters. We further analyze the sensitivity of student performance to distillation settings, highlighting consistent accuracy--efficiency trade-offs when compressing a foundation ECG model into classical and quantum-ready learners under a unified evaluation protocol.

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