Quantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification

TL;DR

This paper explores quantum machine learning techniques applied to classify Progressive Supranuclear Palsy, demonstrating improved accuracy and computational efficiency over classical methods using quantum classifiers and kernel estimators on real quantum hardware.

Contribution

It introduces a quantum machine learning framework for PSP classification that outperforms classical SVMs and validates the approach on both simulators and real quantum devices.

Findings

Quantum classifiers achieved 86% accuracy on PSP data.

Quantum methods outperformed classical SVMs in speed and accuracy.

Successful implementation on IBM quantum hardware.

Abstract

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.