$627,250 in NSF CAREER funding will support Sabek’s five-year “Toward Quantum-Augmented Database Systems” project, which aims to embed quantum processors into database engines for query planning, transaction scheduling and index selection.
The hybrid design targets a core bottleneck in modern databases: classical heuristic and machine-learning optimizers often miss better global solutions, struggle in cold starts and require costly retraining as workloads shift.
Early prototypes from Sabek’s group have shown speedups of more than 10 times over a conventional database optimizer on benchmark queries, while the project will test which database problems can deliver a true quantum advantage.
USC’s broader quantum infrastructure — including more than 10 IBM quantum processors and a D-Wave Advantage system — gives the team hardware access to turn quantum optimization from theory into practical database tools.
This report explores how groundbreaking research led by Assistant Professor Ibrahim Sabek at USC is transforming data management by integrating quantum computing into traditional database systems. As data volumes grow and classical computing faces limits in speed and efficiency, Sabek’s NSF CAREER Award project bridges the gap between quantum theory and practical optimization of data operations. By proposing a hybrid classical-quantum approach, the research marks the beginning of a new era in how data is processed, stored, and retrieved, moving quantum computing from theory into real-world systems engineering.