Date of Award
Spring 2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Iqbal Ahamed Sheikh
Abstract
This research project focuses on developing a quantum sensing system that can detect biomarkers associated with health disorders, like Alzheimer’s and depression. Our goal is to create a sensitive and highly selective quantum sensing device using a diamond nitrogen vacancy (NV) center. To train and test our quantum machine learning algorithms we will preprocess data from the available Human Connectome Project dataset. This dataset forms the basis of our quantum-based methods. The core of our project revolves around developing quantum machine learning algorithms that utilize techniques such as Support Vector Machines and neural networks to diagnose health disorders using data from quantum sensors. The integrated quantum computing resources in our system will efficiently handle the volumes of generated data. We will tailor the quantum algorithms and software for platforms like IBM Qiskit ensuring they are well trained, optimized, precise and efficient in diagnosing these disorders. To evaluate their performance, we will compare them against AI and ML techniques using the Human Connectome Project dataset. In collaboration with health professionals and stakeholders we aim to explore applications while addressing implementation challenges and strategies, for translating our research into clinical practice. Our research project serves as a connection, between quantum technology, machine learning and mental health with the goal of enhancing precision and transforming the way we treat Alzheimer’s disease and depression. This interdisciplinary approach holds promise in improving the level of care and overall results, for individuals grappling with these health conditions.