Modern data-driven research, including artificial intelligence, AI, and a range of applications, rely on database systems to process massive volumes of information in real time, powering critical domains such as finance, healthcare, logistics, and scientific discovery. At the heart of these systems are complex optimization tasks, such as determining how to execute queries or schedule transactions efficiently. As data grows and workloads become more dynamic, these optimization problems become increasingly difficult, often involving an enormous number of possible choices. Current approaches rely on heuristics or machine learning methods that may miss high-quality solutions or require costly retraining of the AI models. Recent advances in quantum hardware have positioned quantum computing as a powerful and promising new computational paradigm for tackling such complex optimization problems. This project explores a new approach that integrates emerging quantum computing technologies into database systems to improve how these optimization tasks are solved. This work has the potential to significantly improve performance and support faster, more reliable data processing in real-world deployments. The project also contributes to workforce development by introducing students to interdisciplinary skills at the intersection of data systems and quantum computing, and by developing educational materials and outreach programs that expand access to computing education and training. TThi