A Reproducible Quantum-Classical Hybrid Protocol for Classifying Parkinson Disease from Human Voice Recordings.

Parkinson Disease is a progressive neurodegenerative disorder for which accessible, low-cost screening methods remain limited. Sustained-vowel voice recordings contain measurable acoustic biomarkers associated with disease-related dysphonia that can be acquired without specialized equipment. This protocol describes a four-qubit quantum-classical hybrid convolutional neural network (QI-HCNN) that combines a parameterized quantum circuit with a shallow classical classification layer to classify Parkinson Disease using dimensionally reduced acoustic voice features. The protocol also benchmarks the proposed approach against a dimensionality-matched classical neural network and gradient-boosted tree classifiers trained on both the reduced and full feature sets. Using a publicly available, de-identified dataset comprising 195 voice recordings from 31 individuals, the QI-HCNN achieved an area under the receiver operating characteristic curve of 0.78 using three-fold stratified cross-validation, compared with 0.87 for the matched classical neural network and 0.94-0.95 for the gradient-boosted baselines trained on the reduced and full feature sets, respectively. A controlled ablation analysis demonstrated that one of the two entanglement operations in the circuit could not influence the measured output by design, whereas the remaining entanglement operation reduced, rather than improved, classification performance relative to a non-entangled variant. Patient-grouped cross-validation further showed that performance estimates varied substantially depending on the individuals assigned to the test set, reflecting the limited cohort size. This protocol therefore provides a fully specified and independently reproducible quantum-classical baseline together with a data-supported evaluation of the strengths and limitations of the current circuit design, providing a methodological foundation for future investigations rather than supporting claims of diagnostic readiness.