Key result
Hybrid quantum-classical CNN-QLSTM shows no accuracy benefit over classical XGBoost.
Why the study?
A comparative evaluation of classical and hybrid quantum-classical neural network models for ECG heartbeat classification across structural scales was needed to assess performance and practical deployment challenges.
Absolute Event Rate: 84% vs 85.07%
Hybrid quantum-classical neural networks currently demonstrate comparable ECG classification accuracy to classical models, but require substantial computational overhead for simulation.
Hybrid quantum models match but do not exceed XGBoost accuracy for ECG classification; leaves open any future hardware advantage.
This research presents a comparative study of classical and hybrid quantum-classical models applied to an Electrocardiogram (ECG) heartbeat classification task. The investigation analyzes (Q)LSTM and (Q)CNN architectures across three distinct structural scales: Small, Medium, and Huge, with all quantum components executed via classical state-vector simulation rather than physical quantum hardware. Benchmarking was conducted on a balanced 2,500-instance subset of the MIT-BIH Arrhythmia Dataset, utilizing an XGBoost model as a non-neural baseline. The empirical results show that hybrid quantum-classical structures can match, and at the Medium scale modestly exceed, purely classical neural networks of comparable parameter count, particularly when deploying the CNN-QLSTM and HYBRID configurations. The top-performing deep learning model was , achieving an 84% test accuracy; this result falls slightly below, and is statistically indistinguishable from, the non-neural XGBoost baseline (85.07%) once sampling uncertainty on the 375-instance test set is taken into account. Substantial computational simulation overhead and a pronounced vulnerability to overfitting at larger structural scales remain the primary barriers to practical deployment. Future work should focus on training and testing on physical QPU hardware to investigate the impact of quantum noise and hardware errors in real-world clinical diagnostics.
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Nikita Saprinskiy (2026) studied ECG heartbeat classification (n=2,500). Hybrid quantum-classical neural networks vs. Classical neural networks and XGBoost was evaluated on Test accuracy. The top-performing hybrid quantum-classical model, CNN_QLSTM, achieved an 84% test accuracy, which was statistically indistinguishable from the non-neural XGBoost baseline of 85.07%.
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