A Comprehensive Review on Quantum Computing Models for Alzheimer’s Disease Prediction

Resumen

Alzheimer’s disease is neurodegerative disorder that affects memory of a person and also the day to day activities of them. Normally, Alzheimer’s disease can be identified by using the MRI scans which consist of high dimensional data. Traditional machine learning models are filed to give higher accuracy while diagnosing the disease. Quantum technologies plays a vital role and its emerging technologies that produce higher accuracy compared to traditional models. In this study we provide a comprehensive review of quantum technology such as Quantum Machine Learning (QML), Quantum Neural Networks (QNN) and Hybrid Quantum Models. Key factors of this study include early identification of Alzheimer’s disease by utilizing the higher dimension of data like MRI images. Further, this article focuses on the application, limitations such as hardware constraints and scalability of clinical decision support system.