USC Viterbi researcher Ibrahim Sabek has won a $627,250 NSF CAREER Award to study how quantum computing can accelerate database optimization, potentially reducing data system processing times from hours to minutes. Modern databases, reliant for decades on heuristic-based systems and rule-based approaches, are increasingly bottlenecked by their inability to find globally optimal solutions as data volumes grow. While machine learning offers some improvement, these approaches struggle with new data and require costly retraining; Sabek proposes a hybrid classical-quantum approach where quantum processors tackle the most complex optimization tasks. “These traditional methods are limited when facing the complexity of modern, large-scale data environments,” explains Sabek, whose research as leader of the Next-generation Data-Intensive Systems Group at USC explores the intersection of database systems and quantum technologies.
Limitations of Current Database Optimization Methods
For decades, database systems have relied on pre-set guidelines programmed by humans, but these approaches are increasingly failing to deliver optimal performance. Known as heuristic-based systems and rule-based approaches, these were foundational to modern computing but now create bottlenecks when confronted with the scale of contemporary data environments. These heuristics, relying on “if X, then Y” logic, are designed for rapid solutions, yet they frequently settle for “locally good but globally suboptimal solutions,” preventing the discovery of more efficient pathways. While machine learning offered a potential remedy, its application to database optimization has considerable hurdles. The technology currently struggles with “cold-start” scenarios, situations where little or no prior data exists, and demands frequent, computationally expensive retraining as workloads evolve. This constant need for recalibration limits its effectiveness in rapidly changing data landscapes, making reliable optimization a persistent challenge.
The issue isn’t a lack of promise, but a practical limitation in adapting to dynamic conditions. USC’s Ibrahim Sabek is addressing these limitations through a novel approach, aiming to integrate quantum computing directly into database engines. This isn’t a complete overhaul; recognizing the current constraints of quantum hardware, Sabek proposes a hybrid system. “Quantum processors tackle the hardest combinatorial subproblems while classical components handle the rest,” he explains, envisioning a coordinated system where quantum capabilities function as an integrated accelerator. Early prototypes from his group have already demonstrated speedups of more than 10 times over a conventional database optimizer on benchmark queries, suggesting a potential pathway to drastically reduce processing times for data-intensive tasks.
Early prototypes from his group have already demonstrated speedups of more than 10 times over a conventional database optimizer on benchmark queries.
The escalating demands placed on modern database systems are prompting a re-evaluation of longstanding optimization techniques. For decades, database management relied on heuristic-based systems and rule-based approaches, but these are now proving inadequate when confronted with exponentially growing data volumes. Recent attempts to leverage machine learning have also encountered obstacles; these systems struggle with “cold-start” scenarios and necessitate frequent, computationally intensive retraining to adapt to shifting workloads. USC’s Ibrahim Sabek is pioneering a novel solution, funded by a $627,250 NSF CAREER Award, that integrates the power of quantum computing with existing classical infrastructure. He aims to develop high-level tools allowing database developers to utilize quantum solvers without requiring specialized quantum expertise. “Through this research, Sabek hopes to move data-intensive systems into a new class of quantum-enhanced technologies capable of meeting growing global data demands with unprecedented speed and efficiency.” Sabek’s work benefits from access to USC’s Quantum Computing Center, including over 10 IBM quantum processors and the D-Wave Advantage system, facilitating the transition from theoretical quantum computing to practical systems engineering.
Heuristics follow fixed patterns and tend to settle for locally good but globally suboptimal solutions, creating a bottleneck that prevents more efficient solutions from being discovered.
Ibrahim Sabek of USC’s Thomas Lord Department of Computer Science is developing a hybrid approach to database optimization, leveraging the unique capabilities of quantum computing to overcome limitations inherent in conventional systems. The potential implications are significant, particularly for large-scale cloud databases serving millions of applications concurrently, where real-time query optimization is critical for performance and resource allocation.
Quantum processors tackle the hardest combinatorial subproblems while classical components handle the rest, all coordinated within the database system optimizer.
