CAREER: Toward Quantum-Augmented Database Systems

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. TThis project develops a framework for quantum-augmented database systems by developing and tightly integrating hybrid quantum-classical optimization within database engines. The research focuses on four main components. First, it designs high-level abstractions for expressing database optimization problems in forms compatible with quantum solvers while preserving domain-specific constraints. Second, it develops scalable hybrid optimization methods using feedback from quantum sampling. Third, it builds co-optimization techniques that treat quantum-based methods as first-class components within traditional query optimizers, enabling adaptive selection and configuration under performance constraints. Fourth, it introduces storage and state management techniques for handling large optimization models within database systems. The framework will be implemented and evaluated using real-world benchmarks and integrated into open-source database platforms. The expected outcome is a new class of accelerators for data management systems that incorporate quantum computing for solving large-scale optimization problems, along with open-source software and educational modules that support student training, expand access to computing education, and enable both researchers and practitioners to adopt quantum-enhanced data system technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2544715 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Ibrahim Sabek | Institution: University of Southern California, LOS ANGELES, CA | Award Amount: $439,075 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2544715 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2544715.html