Quantum Computing Achieves Database Optimisation with Sub-5 Second Runtime Performance

Database optimisation, crucial for managing ever-increasing data volumes, presents a significant challenge as traditional methods struggle with complex workloads. Hanwen Liu and Ibrahim Sabek, both from the University of Southern California, alongside their colleagues, demonstrate a potential breakthrough by exploring the integration of quantum computing into real-time database systems. Their research addresses a key limitation of previous work , the high latency of quantum services , by leveraging new, low-latency solvers like NL-Solver to build Q2O, the first quantum-augmented query optimiser. This innovative system encodes join order problems into a format directly solvable by quantum hardware, offering the promise of significantly faster and more efficient database management , a vital step towards harnessing the power of quantum computation for practical, everyday application.

This innovative approach addresses the escalating challenges of optimising complex queries in rapidly growing datasets, a problem that has become exponentially harder to solve efficiently using classical methods. The core of Q2O lies in its ability to encode the join order problem, a critical aspect of query optimisation, as a nonlinear model compatible with low-latency quantum solvers like NL-Solver.

The team’s methodology involves translating the quantum solution into a plan hint, effectively guiding PostgreSQL’s existing optimizer to generate a complete and optimised query plan. This end-to-end workflow allows Q2O to handle actual queries in real time, a feat previously hindered by the high overhead associated with traditional quantum computing services, such as the 5-second runtime minimum of the D-Wave CQM-Solver. Experiments utilising the Join Order Benchmark (JOB) workload reveal that Q2O yields speedups on 31 out of 113 queries, achieving a maximum latency reduction of 92.7% and an average reduction of 42.09% on those improved queries. This substantial performance gain stems from the quantum annealer’s capacity to efficiently explore vast search spaces and escape local optima through quantum tunneling, a process impossible for classical algorithms.
Further analysis of the system’s performance demonstrates significant improvements in end-to-end latency, despite the unavoidable overhead of cloud communication with the NL-Solver. Table I details per-component latencies for four JOB queries, showing execution-time speedups of up to 13.15× and overall E2E latency reductions of up to 1.42× when compared to standard PostgreSQL optimisation. The research establishes a crucial balance between efficiency and solution quality, proving that quantum augmentation can deliver tangible benefits within a practical database setting. This work opens exciting possibilities for tackling even larger and more complex database optimisation problems in real-time, potentially revolutionising data management and analytics.

Nonlinear Modelling of Join Orders for Quantum Annealing

0.4, configured with a pg hint plan version, to facilitate this integration. Researchers harnessed the Join Order Benchmark (JOB) workload to evaluate Q2O’s performance against PostgreSQL’s default query plans on real-world optimization challenges. The team measured query plan quality by assessing execution time, revealing that Q2O achieved speedups on 31 out of 113 queries, with a maximum latency reduction of 92.7% and an average reduction of 42.09% on those improved queries. To quantify the system’s performance, the team meticulously measured end-to-end (E2E) latency across the entire pipeline.

For PostgreSQL, this comprised planning time and execution time, while Q2O’s E2E latency included PostgreSQL planning with hints, PostgreSQL execution, and the time taken by the NL-Solver. Table I details per-component latencies for four JOB queries, demonstrating execution time speedups of up to 13.15x and overall E2E latency reductions of up to 1.42x. Despite the unavoidable overhead of cloud communication with the NL-Solver, the research conclusively shows that Q2O’s higher-quality join order solutions deliver substantial performance gains. This work establishes that quantum computing can be effectively integrated into database management systems to achieve real-time optimization.

Q2O speeds joins using quantum annealing

Scientists have achieved a breakthrough in query optimization by integrating quantum annealing into a fully functional database management system. The research team presents Q2O, the first real Quantum-augmented Query Optimizer, demonstrating the feasibility of leveraging quantum computing for real-time database tasks. Experiments reveal that Q2O effectively encodes the join order problem as a nonlinear model, compatible with the NL-Solver, utilising actual database statistics to guide PostgreSQL’s query planning process. This innovative workflow allows Q2O to handle live queries and deliver substantial performance improvements.

Results demonstrate significant speedups on a Join Order Benchmark (JOB) workload, with Q2O yielding faster execution on 31 out of 113 queries. The team measured a maximum latency reduction of 92.7% and an average reduction of 42.09% for these optimised queries. Data shows substantial improvements across various queries, with gains ranging from 13.1% to 91.6% in execution time compared to PostgreSQL’s default plans. Specifically, query q7 experienced a 91.6% improvement, while q21 saw a remarkable 92.7% reduction in latency. Detailed analysis of end-to-end (E2E) latency reveals a breakdown of performance components.

For query q21, PostgreSQL exhibited a planning time of 1.00ms and an execution time of 3581.40ms, whereas Q2O achieved a planning time of 2.24ms, an execution time of 272.35ms, and an NL-Solver time of 2530.22ms. This resulted in a 13.15x increase in execution time and a 1.28x improvement in overall E2E latency. Table I confirms these gains across multiple queries, including q60, q62, and q63, with execution time speedups of 1.82x, 10.89x, and 8.28x respectively. Measurements confirm that despite the overhead introduced by the NL-Solver, primarily due to cloud communication, Q2O consistently produces higher-quality join order solutions. The team recorded E2E latency reductions of up to 1.42x for queries q62 and q63. This work establishes a foundation for tackling larger-scale and more complex database optimisation challenges in real-time, paving the way for future advancements in quantum-augmented data management system.

Q2O delivers practical quantum database speedups for specific

Scientists have developed Q2O, a novel quantum-augmented query optimizer capable of handling real-time database queries. This system encodes the join order problem as a nonlinear model, compatible with the NL-Solver, and translates the solution into a plan hint for PostgreSQL’s optimizer, ultimately generating a complete query plan. Through experiments using the Join Order Benchmark, Q2O demonstrated speedups on 31 out of 113 queries, achieving a maximum latency reduction of 92.7% and an average reduction of 42.09% for those improved queries. The significance of this work lies in successfully bridging the gap between theoretical quantum optimization and practical database systems.

While prior research focused on abstract representations of database problems for quantum computation, Q2O represents the first end-to-end implementation within a DBMS, leveraging recent advancements in low-latency quantum annealing solvers. Results indicate substantial gains in query execution time, up to 13.15times faster, and overall end-to-end latency, despite the unavoidable overhead of cloud-based quantum processing. The authors acknowledge that the NL-Solver introduces communication latency, representing a current limitation. Future research directions involve extending this framework to address larger-scale and more complex database optimization challenges in real-time. This pioneering work establishes a promising pathway for integrating quantum computing into database management systems, potentially revolutionizing query processing and data analytics.