Optimal join order sequence significantly impacts query execution performance.Because the search space grows exponentially with the number of relations involved in the queries, DP-based approaches can compute optimal plans only for queries with a small number of relations.Many heuristic methods trade off solution quality for computational efficiency.Quantum computing excels at solving combinatorial optimization problems such as join order optimization.However, existing quantum-related methods either focus solely on left-deep join trees-thus narrowing the problem scope-or support bushy join trees but are constrained by quantum hardware and cannot scale to large queries.Importantly, neither branch has been integrated into an actual database system.In this paper, we present a hybrid quantum-classical optimization approach for bushy join trees, integrated within a real database system (PostgreSQL).This approach expands the search space for join order sequences and overcomes previous limitations.Evaluation on the Join Order Benchmark shows that our method can reduce query execution time by up to 92.7% and achieve a 1.42x improvement in end-to-end latency.
Optimal join order sequence significantly impacts query execution performance.Because the search space grows exponentially with the number of relations involved in the queries, DP-based approaches can compute optimal plans only for queries with a small number of relations.Many heuristic methods trade off solution quality for computational efficiency.Quantum computing excels at solving combinatorial optimization problems such as join order optimization.However, existing quantum-related methods either focus solely on left-deep join trees-thus narrowing the problem scope-or support bushy join trees but are constrained by quantum hardware and cannot scale to large queries.Importantly, neither branch has been integrated into an actual database system.In this paper, we present a hybrid quantum-classical optimization approach for bushy join trees, integrated within a real database system (PostgreSQL).This approach expands the search space for join order sequences and overcomes previous limitations.Evaluation on the Join Order Benchmark shows that our method can reduce query execution time by up to 92.7% and achieve a 1.42x improvement in end-to-end latency.
Abstract The join order has a huge impact on the execution time of a query, such that finding an optimal join order plays a crucial role in query optimization. However, join order optimization is known to be NP-hard. Hence, in this paper, we propose an approach for accelerating join order optimization by quantum computers. We extend our previous approach supporting bushy join trees by splitting the search space of possible join orders and solving each of these subspaces on currently available quantum computers to optimize the join of more relations than our previous approach. We have integrated our approach to quantum query optimization in the relational database management system PostgreSQL to conduct studies with real-world queries. In our experiments, we show that we can perform join order optimization up to 7 relations for real-world queries using quantum annealing and up to 8 relations for artificial queries using simulated annealing with a reasonable number of QUBO problems solved by D‑Wave’s Quantum Annealer. Furthermore, we show that our approach can be also used to perform join-order for queries joining five relations on circuit-based quantum computers running the quantum approximate optimization algorithm (QAOA) and variational quantum eigensolver (VQE) approaches.
Efficient query optimization is crucial for relational database systems, especially for optimizing join orders in complex queries. This work introduces a hybrid approach that integrates Eliminating Cartesian Products (ECP) with splitting the QUBO search space (SQSS) to reduce the size of the QUBO problem, minimizing binary variables and constraints. This improves the performance of the quantum algorithm while lowering hardware requirements. We evaluate our method using real-world SQL queries from the ErgastF1 dataset on quantum and classical algorithms, including Quantum Annealing (QA), Simulated Annealing (SA), QAOA, and VQE, implemented on D-Wave's Quantum Annealer and universal gate-based simulators. Additionally, we analyze the impact of selectivity and SQSS on QUBO weight distribution and algorithmic performance, highlighting optimization efficiency for QA and SA. Experimental results show consistent optimal join orders and enhanced query optimization for various selectivity conditions, and they also highlight the limitations of current quantum hardware for complex queries. This study further confirms the potential of hybrid quantum-classical methods for scalable quantum-enhanced database optimization.
The join order (JO) optimization problem is a key challenge in query optimization. Classical approaches can compute the optimal solution for smaller queries. For larger queries, some heuristic methods trade off plan quality to reduce the exponential search space. Recently, quantum-based methods have been proposed to leverage quantum mechanisms to accelerate exploration; however, encoding problem-specific constraints as penalty terms introduces extra overhead. Moreover, quantum-inspired methods on classical hardware do not harness the true advantages of quantum computation. Furthermore, these methods remain at the simulation stage. In this demonstration, we present the first Quantum-augmented Query Optimizer ( Q 2 O ) that integrates a hybrid quantum-classical approach to solve the JO problem in a real database setup. This demonstration allows conference attendees to interact directly with Q 2 O by executing queries and viewing detailed execution results (Scenario 1). Users can easily compare plan quality between Q 2 O and PostgreSQL (Scenario 2). Additionally, they can experiment with quantum parameters to observe their impact (Scenario 3). Experimental results show that the query plan generated by our approach achieves up to a 13x speedup in query execution. The video corresponding to this demonstration is available at this link.
The join order is one of the most important factors that impact the speed of query processing. Its optimization is known to be NP-hard, such that it is worth investigating the benefits of utilizing quantum computers for optimizing join orders. Hence in this paper, we propose to model the join order problem as quadratic unconstrained binary optimization (QUBO) problem to be solved with various quantum approaches like QAOA, VQE, simulated annealing and quantum annealing. In this paper, we will show the enhanced performance using our QUBO formulation to find valid and optimal shots for real-world queries joining three, four and five relations on D-Wave quantum annealer. The highlight of this study will demonstrate that we were able to generate valid and optimal join orders for five relations using the D-Wave quantum annealer, with values of about 12% and 0.27% respectively and we were able to find the optimal bushy join trees. Experiments for the gate-based simulations were able to produce optimized join order for three and four relations including bushy trees using QAOA and VQE algorithms.
Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific attention. Yet, it is an open question if they can achieve sustainable, practical advantages with improved quantum processors. In this paper, we present a novel approach that uses quantum reinforcement learning (QRL) for JO based on a hybrid variational quantum ansatz. It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer qubits, which is a scarce resource even for post-NISQ systems. Despite moderate circuit depth, the ansatz exceeds current NISQ capabilities, requiring an evaluation by numerical simulations. While QRL may not significantly outperform classical approaches in solving the JO problem with respect to result quality (albeit we see parity), we find a drastic reduction in required trainable parameters. This benefits practically relevant aspects ranging from shorter training times compared to classical RL, less involved classical optimisation passes, or better use of available training data, and fits data-stream and low-latency processing scenarios. Our comprehensive evaluation and discussion delivers a balanced perspective on possible practical quantum advantage, provides insights for future systemic approaches, and allows for quantitatively assessing trade-offs of quantum approaches for a crucial problem of database management systems.
The rapidly increasing number of heterogeneous data ecosystems, such as structured databases, semi-structured data lakes, real-time streams, distributed knowledge graphs, has further complicated the issue of query planning and optimization in modern information systems extensively. The optimization of queries is essentially an NP-hard combinatorial problem especially when based on multi-join queries, distributed execution plan, dynamic cost model, and uncertainty in the data access patterns. Even decades of concept and engineering advances, classical database optimizers are rapidly becoming unable to offer a globally optimal execution plan when faced with high-dimensional data, high data volatility, with a very tight latency constraint. Good news Recent breakthroughs in quantum computing propose promising computational paradigms that can be used to solve combinatorial optimization problems via quantum parallelism, superposition, and entanglement. Nevertheless, even the near-term quantum hardware is limited in the number of available qubits, noise, and decoherence, making it impractical to achieve fully quantum database systems in any perceived future. This is also the reason why quantum-classical hybrid algorithms are being developed: designed to add quantum optimization subroutines to classical database architectures is guaranteed without breaking their compatibility with existing execution engines. The following paper presents a framework of scalable quantum-classical hybrid computing of complex query planning in heterogeneous data ecosystems. The framework breaks down query optimization into classical and quantum-amenable optimization, which allows NP-hard subproblems, such as join ordering, operator placement, and cost minimization, to be transformed into quantum optimization models such as Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians. Classical elements handle query parsing, semantic analysis, constraint verification, and orchestration, whereas quantum processors are called selectively to solve combinatorial decision-making tasks. The suggested architecture will consist of a layered execution, hybrid execution, cost-model encoding approach, and adaptive fallback mechanisms to make them robust to quantum hardware variability. This has been experimentally tested with simulated quantum annealing and variational quantum algorithms that exhibit a quantifiable improvement in the quality and scalability of optimization and heuristics based on entirely classical algorithms, especially in high-dimensional query plans. The findings confirm the practicability of quantum query optimization and put down a feasible route to implement quantum algorithms in the serving machine of the next-generation data management systems.