Transferability of Quantum Feature Maps from Simulation to Hardware in Healthcare Data
Quantum machine learning is often proposed for richer feature representations, yet most evidence rests on idealized simulation rather than real noisy intermediate-scale quantum (NISQ) hardware. This research presents a controlled comparison of classical and quantum-enhanced diagnostic pipelines on three clinical binary classification tasks: Mammographic Mass, Anemia, and Diabetic Retinopathy. All pipelines share standardized preprocessing, principal component analysis (PCA), and a fixed extreme gradient boosting (XGBoost) classifier, so differences arise only from the feature representation. Four quantum encodings (angle, phase, basis, and the ZZ feature map) are each run on two backends: a noiseless simulator and the real IBM Heron r2 processor (156 qubits). Across nine performance metrics, compared with the classical pipeline, the performance decreases in the quantum hardware execution for all datasets, with a more significant reduction observed for the Anemia dataset. In contrast, compared with the simulated pipeline, the hardware execution shows a slight performance decrease for the Mammographic and Diabetic Retinopathy datasets. The exceptions are the angle and phase encodings for the Mammographic dataset, where the hardware result improves slightly compared with the simulator. For the Anemia dataset, the transition from simulation to real quantum hardware results in a considerable performance reduction. These findings show that current quantum feature maps through the encode–measure–boost pipeline on NISQ hardware do not yet outperform a well-designed classical pipeline.