Evaluation and optimization of neurological data sets in quantum feature maps and comparison with conventional classical methods

Early diagnosis of Alzheimer's disease (AD) poses a great difficulty for patients due to the lack of sensitivity and accuracy of traditional screening and diagnostic methods. This work addresses the issue by comparing state-of-the-art quantum machine learning (QML) approaches in early detection of AD with traditional machine learning techniques, such as support vector machines (SVMs). The effectiveness of various techniques in accurately and rapidly identifying AD is evaluated using a database of people with the disease. The problem is the low accuracy in early detection of AD. The objective is to evaluate the effectiveness of various machine learning techniques in early identification of the disease using a database of AD patients. We demonstrate that the use of machine learning algorithms - most notably, SVM - can be a useful tool for accurate early identification of AD, even in specialized populations. Furthermore, the potential of QML to improve traditional algorithms implies that there is substantial collaboration between classical and quantum techniques in the field of machine learning. In summary, the integration of traditional and QML techniques could lead to a remarkable advance in the early identification of AD, which would benefit the field of medicine and open new avenues for the study of this neurodegenerative disease.