Benchmarking Classical and Quantum-Hybrid Clustering on Autism Spectrum Disorder Screening Data

Quantum Machine Learning (QML) has been proposed as a framework that may offer theoretical advantages over classical machine learning, especially in computational complexity and parallel processing of high-dimensional data. However, due to the limitations imposed by the Noisy Intermediate-Scale Quantum (NISQ) era, implementing large-scale quantum algorithms remains infeasible, making hybrid quantum-classical approaches a more viable alternative. In this work, we formulate and implement custom quantum-hybrid versions of classical clustering algorithms and compare their performance on an Autism Spectrum Disorder (ASD) screening dataset, which represents a highly relevant clinical domain characterized by a complex mix of behavioral and demographic features. We evaluate classical k-means, DBSCAN, spectral clustering, and agglomerative clustering against these formulated quantum-hybrid implementations. For the evaluation, we use inner metrics (Silhouette, Davies-Bouldin, Calinski-Harabasz) and outer metrics (AMI, ARI) on the generated partitions. The results show that the formulated hybrid implementations achieve a partition quality comparable to that of classical counterparts, except in the case of Q-DBSCAN, where the classical algorithm outperformed our hybrid implementation due to the high-dimensional mapping. Thus, we present empirical information about strengths, weaknesses and potential viability of these formulated hybrid implementations in real-life applications in the NISQ era.