  Machine learning and quantum computing are being progressively explored to
shed light on possible computational approaches to deal with hitherto
unsolvable problems. Classical methods for machine learning are ubiquitous in
pattern recognition, with support vector machines (SVMs) being a prominent
technique for network classification. However, there are limitations to the
successful resolution of such classification instances when the input feature
space becomes large, and the successive evaluation of so-called kernel
functions becomes computationally exorbitant. The use of principal component
analysis (PCA) substantially minimizes the dimensionality of feature space
thereby enabling computational speed-ups of supervised learning: the creation
of a classifier. Further, the application of quantum-based learning to the PCA
reduced input feature space might offer an exponential speedup with fewer
parameters. The present learning model is evaluated on a real clinical
application: the diagnosis of Progressive Supranuclear Palsy (PSP) disorder.
The results suggest that quantum machine learning has led to noticeable
advancement and outperforms classical frameworks. The optimized variational
quantum classifier classifies the PSP dataset with 86% accuracy as compared to
conventional SVM. The other technique, a quantum kernel estimator, approximates
the kernel function on the quantum machine and optimizes a classical SVM. In
particular, we have demonstrated the successful application of the present
model on both a quantum simulator and real chips of the IBM quantum platform.
