  Alzheimer's disease (AD) is the most prevalent neurodegenerative brain
disorder, which results in significant cognitive impairments, especially in the
elderly population. Cognitive impairments can manifest as a decline in various
mental faculties, such as concentration, memory, and other higher-order
cognitive abilities. These deficits can significantly impact an individual's
capacity to comprehend information, acquire new knowledge, and communicate
effectively. One of the affected activities due to cognitive impairments is
handwriting. By analyzing different aspects of handwriting, including pressure,
velocity, and spatial organization, researchers can detect subtle alterations
that might indicate early-stage cognitive impairments, especially AD. Recently,
several classical artificial intelligence (AI) approaches have been proposed
for detecting AD in elderly individuals through handwriting analysis. However,
advanced AI methods require more computational power as the size of the data
increases. Additionally, diagnoses can be influenced by factors such as limited
relevant classical vector space and correlations between features. Recent
studies have shown that using quantum computing technologies in healthcare can
not only address these problems but also accelerate complex data analysis and
process large datasets more efficiently. In this study, we introduced a
variational quantum classifier with fewer circuit elements to facilitate the
early diagnosis of AD in elderly individuals based on handwriting data. We
employed ZZFeatureMap for encoding features. To classify AD, a parameterized
quantum circuit consisting of repeated Ry and Rz rotation gates, as well as CY
and CZ two-qubit entangling gates, was designed and implemented. The proposed
model achieved an accuracy of 0.75 in classifying AD.
