将大模型ECGFounder的知识蒸馏到小模型，实现高效心电图分类。
From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student
- 用大模型做教师，蒸馏知识到轻量级经典与量子兼容学生模型。
- 在两个数据集上，学生模型参数减少90%以上，性能仍接近教师。
- 首次统一评估经典与量子模型的压缩效果，适合边缘部署和量子计算研究者。
基础模型近期提升了心电图（ECG）表征学习能力，但其部署受限于计算成本与延迟。本文将ECGFounder作为高容量教师模型，在PTB-XL和MIT-BIH心律失常数据库上进行二分类微调，并探究知识蒸馏能否将其预测行为迁移到紧凑的学生模型。我们评估了两种经典1D学生模型（ResNet-1D 和 轻量级CNN-1D）以及一个量子就绪流程：结合卷积自编码器将256样本心电窗口压缩至低维隐空间，并使用Qiskit实现的6量子比特变分量子电路在模拟后端执行。在两个数据集中，教师表现最优；蒸馏后的学生模型在可训练参数大幅减少的情况下仍保持竞争力。进一步分析表明，学生性能对蒸馏设置敏感，且在统一评估协议下，压缩过程始终存在精度-效率权衡。该研究为心电图模型的轻量化与量子化部署提供了新范式。
原文摘要 · Abstract (English)
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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