Neurological disorders are among the leading causes of disability and death worldwide. The WHO’s Global status report on neurology shows that neurological conditions affect more than 40% of the global population. One of the actions needed to be done, is to strengthen data systems and monitoring for evidence-informed decision-making and accountability.

The intersection of quantum computing and healthcare is leading to breakthroughs in medical devices that promise to revolutionize diagnostics, treatment, and patient care. By leveraging quantum principles, medical devices are becoming more efficient, precise and capable of handling complex tasks that were once impossible. Traditional Machine Learning models struggle with high-dimensional and complex medical datasets, which led interest towards quantum machine learning (QML). By leveraging quantum computing principles, QML may offer advantages in handling such data and outperform classical models in specific tasks.

Here, we compare Machine Learning classifiers with Quantum Machine Learning classifiers to investigate the applicability; focusing on Neural Networks for classification of neural conditions.

The results show, that QML models require further refinement and problem-specific adaptations to be competitive. While some literature indicates that QML can outperform classical methods, this study highlights the need for tailored quantum algorithms and further research to fully exploit quantum computing's potential for medice.