Quantum Machine Learning Appliances for Tackling Neurological Disorders

Abstract

Neurological disorders are among the leading causes of disability and death worldwide. According to the WHO Global Status Report on Neurology, more than 40 percent of the global population is affected by neurological conditions. Addressing this growing burden requires stronger data systems and improved monitoring to enable evidence based decision making and accountability in healthcare systems.

At the same time, advances at the intersection of quantum computing and healthcare are opening new possibilities for medical technologies. Quantum computing principles are being explored to enhance diagnostic tools, treatment planning, and patient care by improving computational efficiency and precision. Classical machine learning models often struggle with high dimensional and complex medical datasets, particularly in neurology. This challenge has driven interest in quantum machine learning, which may offer advantages in processing and analyzing such data and could outperform classical approaches in specific tasks.

This study compares classical machine learning classifiers with quantum machine learning classifiers, focusing on the classification of neurological conditions. The results indicate that quantum models still require further refinement and problem specific adaptation to become competitive. Although prior research suggests potential advantages, tailored quantum algorithms and continued investigation are necessary to fully realize the promise of quantum computing in medicine.