Quantum Circuit Algorithm for Heart Disease Detection
DOI:
https://doi.org/10.48048/tis.2025.10520Abstract
This study investigates hybrid quantum-classical K-Means algorithms for clustering heart disease patient data. Using quantum swap-test circuits for distance calculation, 2 approaches were tested on both noisy and ideal quantum simulators. A real-world dataset of over 1,000 records was preprocessed using standard techniques, including normalization, outlier removal, and dimensionality reduction. The results show that both quantum methods achieve accuracy up to 0.83 and F1-scores comparable to the classical K-Means baseline (0.82 - 0.83), even when executed on quantum simulators with real-device noise models. These findings highlight the practical potential of quantum-enhanced clustering methods. The study supports the feasibility of applying quantum-enhanced clustering in medical analytics, particularly for early-stage heart disease detection and patient risk stratification.
HIGHLIGHTS
- This paper introduces a hybrid quantum–classical K-Means clustering framework for analyzing heart disease data.
- Two quantum circuits for distance calculation are designed and evaluated on a real-world dataset using both ideal quantum simulators and noisy quantum hardware (e.g., IBM’s ibm_brisbane).
- The quantum clustering algorithms demonstrate performance comparable to classical K-Means and traditional models such as XGBoost.
- Future work will focus on circuit optimization, scalable encoding strategies, and validation on larger datasets using real quantum devices.
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