Semester of Graduation
Fall 2026
Degree Type
Dissertation/Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science
Committee Chair/First Advisor
Dr. Abhishek Parakh
Second Advisor
Dr. Michail Alexiou
Third Advisor
Dr. Manohar Raavi
Abstract
As quantum computing and networking transition into engineering reality, the rapid demand for skilled "Quantum Scientists" is hindered by the steep cognitive barriers of abstract quantum mechanics. This issue is compounded further by the current state of affairs in education, in which the necessary pedagogical materials suffer from a drastic lack of integration due to their rigid reliance on static textbooks, simulation scripts, and visualisations hosted via separate websites or tools.
This research presents the innovative educational platform named AQUIRE, which addresses the previously mentioned problem through its novel architectural implementation of gamification features and a quantum network simulation software embedded inside a single Jupyter notebook. The ability to provide instant context-aware help is achieved by the incorporation of a dual-agent Artificial Intelligence system, powered by the latest technology in Retriever Augmented Generative pipelines, as well as a dynamic curriculum generation algorithm that personalizes lessons according to student's prior knowledge and time constraints.
Pedagogical and algorithmic evaluation confirms that such approach not only accurately represents the underlying principles of quantum mechanics, but also drastically improves the effectiveness of instruction by modeling such complex concepts as decoherence and entanglement.
Included in
Artificial Intelligence and Robotics Commons, Cybersecurity Commons, Other Computer Sciences Commons, Software Engineering Commons, Systems Architecture Commons