Optimized Quantum Classifiers for the Prevention of Anxiety Disorders Using Wearable Data

Quantum machine learning (QML) provides a framework for benchmarking wearable biosignal classification relevant to stress detection. Motivated by the burden of stress-related conditions, this study compares three quantum classifiers with seven classical baselines using heart rate and respiration rate features as inputs under noise-free and noisy conditions. Uncertainty was quantified using Nadeau–Bengio-corrected confidence intervals and percentile bootstrap (B=1000). The variational quantum classifier (VQC) achieved an accuracy of 99.47%/97.30% (noise-free/noisy), the quantum support vector classifier (QSVC) achieved 99.90%/99.37%, and PegasosQSVC achieved 99.80%/99.70%. Additionally, under the assessed proof-of-concept conditions, statistical equivalence between the QSVC and the best-performing classical model was established at Δ=1 pp; PegasosQSVC under noise achieved equivalence at Δ=2 pp with accuracy degradation of less than 0.10 pp. The time feature was identified as the primary separability driver in a post hoc classical ablation. Tree-based models were robust on physiological features alone. The surveyed methods provide a reproducible, noise-aware benchmark for wearable physiological signal classification; however, the reported high accuracies are based on a deliberately separable proof-of-concept benchmark and do not demonstrate clinical utility or a quantum advantage.