Key result
Quantum kNN matches classical methods for hypertension prediction, achieving ~79% F1-score.
Why the study?
This study evaluated a quantum-enhanced k-Nearest Neighbors classification algorithm using a fidelity-derived quantum dissimilarity measure for detecting arterial hypertension.
Does a fidelity-derived quantum dissimilarity-enhanced k-Nearest Neighbors algorithm improve the detection of arterial hypertension compared to a classical k-Nearest Neighbors algorithm in adults aged 30-80 years without cardiovascular disease?
Cross-Sectional (n=1,091)
Yes
Does a fidelity-derived quantum dissimilarity-enhanced k-Nearest Neighbors algorithm improve the detection of arterial hypertension compared to a classical k-Nearest Neighbors algorithm in adults aged 30-80 years without cardiovascular disease?
This simulation-based proof-of-concept demonstrates that a fidelity-derived quantum dissimilarity k-Nearest Neighbors algorithm using dense-angle encoding of clinical and ECG features achieves classification performance comparable to classical kNN for detecting arterial hypertension.
Answers are grounded in this paper and cite their sources.
Comparable performance offers no basis for clinical adoption; leaves open whether quantum metrics improve hypertension detection.
We present a quantum-enhanced version of the classic k-Nearest Neighbors (kNN) classification algorithm, applied to the detection of arterial hypertension. The traditional Euclidean distance metric of the kNN algorithm is replaced with a fidelity-derived quantum dissimilarity measure to evaluate the similarity between data samples. We map classical real-world clinical and ECG-derived data features into quantum states via the dense-angle encoding, which efficiently packs multiple features into minimal qubits while maintaining pure states. We evaluate the performance of the dissimilarity measure using both the noiseless state-vector Simulator and the IBM Qiskit Estimator primitives on 25 test sets. We found that the quantum approaches present a lower mean number of neighbors (6.8 ± 4.1), while the classical approach has a mean of (7.9 ± 4.4) neighbors. The classical kNN algorithm achieves a balanced accuracy of (70.9 ± 2.9)% and an F1-macro of (71.5 ± 3.0)%, while the quantum algorithm scores (67.9 ± 2.4)% and (68.3 ± 2.4)%, respectively, indicating a marginally non-statistically significant difference with a p-value of 6.6%. In addition, the classical kNN algorithm with cosine similarity achieves performance equivalent to the quantum dissimilarity metric, with a p-value of 75.5% for the F1-macro metric. While the quantum algorithm does not claim computational supremacy over the classical baseline, the framework provides a promising simulation-based proof of concept that fidelity-based similarity is a physically meaningful approach to hybrid quantum-classical classification.
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Tampakaki et al. (2026) conducted a cross-sectional in Essential arterial hypertension (n=1,091). Fidelity-derived quantum dissimilarity k-Nearest Neighbors (kNN) algorithm vs. Classical Euclidean distance k-Nearest Neighbors (kNN) algorithm was evaluated on F1-score for arterial hypertension prediction (5-fold cross-validation). Fidelity-derived quantum dissimilarity kNN achieved a 79.2% F1-score and 72.5% accuracy for hypertension prediction, comparable to the 82.1% F1-score and 75.5% accuracy of classical Euclidean kNN.
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