Large-scale database optimization problems can be encoded as Quadratic Unconstrained Binary Optimization (QUBO) instances for execution on quantum hardware. As the problem size grows, these QUBOs quickly exceed the capacity of current devices and must be decomposed into multiple subproblems. However, decomposition does not eliminate global dependencies: subproblems remain coupled through boundary interfaces, and overall solution quality depends critically on how partial solutions are fused. In this demonstration, we present QFusion , the first boundary-aware fusion planning and execution framework for large-scale QUBO solving that runs on an actual quantum annealer. We propose a DBMS-inspired, cost-based fusion tree that explicitly selects the fusion order and merge strategy. QFusion supports three database optimization tasks: join order optimization, multiple query optimization, and index selection. Conference attendees can interactively control the complete workflow through three designed scenarios: inspecting the construction of QUBOs with their database semantics, adjusting the decomposition granularity to observe the resulting boundary interfaces, and configuring fusion planning to examine the execution results on an actual quantum annealer. Overall, the demonstration highlights that scalable QUBO solving is not only a matter of solving local subproblems, but also a systems problem that requires boundary-aware fusion planning. The video corresponding to this demonstration is available at this link.
Large-scale database optimization problems can be encoded as Quadratic Unconstrained Binary Optimization (QUBO) instances for execution on quantum hardware. As the problem size grows, these QUBOs quickly exceed the capacity of current devices and must be decomposed into multiple subproblems. However, decomposition does not eliminate global dependencies: subproblems remain coupled through boundary interfaces, and overall solution quality depends critically on how partial solutions are fused. In this demonstration, we present QFusion , the first boundary-aware fusion planning and execution framework for large-scale QUBO solving that runs on an actual quantum annealer. We propose a DBMS-inspired, cost-based fusion tree that explicitly selects the fusion order and merge strategy. QFusion supports three database optimization tasks: join order optimization, multiple query optimization, and index selection. Conference attendees can interactively control the complete workflow through three designed scenarios: inspecting the construction of QUBOs with their database semantics, adjusting the decomposition granularity to observe the resulting boundary interfaces, and configuring fusion planning to examine the execution results on an actual quantum annealer. Overall, the demonstration highlights that scalable QUBO solving is not only a matter of solving local subproblems, but also a systems problem that requires boundary-aware fusion planning. The video corresponding to this demonstration is available at this link.
Database systems encompass several performance-critical optimization tasks, such as join ordering and index tuning. As data volumes grow and workloads become more complex, these problems have become exponentially harder to solve efficiently. Quantum computing, especially quantum annealing, is a promising paradigm that can efficiently explore very large search spaces through quantum tunneling. It can escape local optima by tunneling through energy barriers rather than climbing over them. Earlier works mainly focused on providing an abstract representation (e.g., Quadratic Unconstrained Binary Optimization (QUBO)) for the database optimization problems (e.g., join order) and overlooked the real integration within database systems due to the high overhead of quantum computing services (e.g., a minimum 5s runtime for D-Wave's CQM-Solver). Recently, quantum annealing providers have offered more low-latency solutions, e.g., NL-Solver, which paves the road to actually realizing quantum solutions within DBMSs. However, this raises new systems research challenges in balancing efficiency and solution quality. In this talk, we show that this balance is possible to achieve. As a proof of concept, we present Q2O, the first real Quantum-augmented Query Optimizer. We show the end-to-end workflow: we encode the join order problem as a nonlinear model, a format solvable by the NL-Solver, using actual database statistics; the solution is translated into a plan hint that guides PostgreSQL's optimizer to produce a complete plan. Q2O is capable of handling actual queries in real time.
Quantum annealers offer a promising sampling-based computing paradigm for large-scale combinatorial optimization. However, directly using them to solve database optimization problems in real-time scenarios remains challenging due to strict hardware constraints and the opacity of commercial hybrid solvers. To address these challenges, we present QDBO , a white-box quantum-augmented database optimizer that directly leverages low-level quantum sampling on a quantum annealer. It first performs an initial reduction to produce embeddable QUBO instances via multiple mapping strategies that trade off efficiency and quality. It then applies an online iterative relaxation correction procedure that uses sampling feedback to correct the reduced QUBO across iterations, enabling controllable quality improvements under varying time budgets. We integrate QDBO with the PostgreSQL query optimizer to construct Q 2 O-QDBO for join order optimization. Evaluations on the Join Order Benchmark (JOB) and the Cardinality Estimation Benchmark (CEB) show that Q 2 O-QDBO improves 62 out of 113 JOB queries, and achieves up to 49.36× execution-time speedup and 36.88× end-to-end speedup on the CEB workload. Evaluations on synthetic queries with up to 50 relations show that Q 2 O-QDBO achieves significantly less end-to-end latency while maintaining similar solution quality compared with black-box quantum solvers. To demonstrate generality, we further integrate QDBO with PostgreSQL for index selection on TPC-H, where QDBO achieves up to 2.64× workload speedup over the empty-index baseline and outperforms classical and black-box solver baselines. These results demonstrate that using QDBO to solve large-scale database optimization problems in real-time settings is promising.
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 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.
Large Internet-of-Things (IoT) installations increasingly place relational databases at gateways, edge servers, application servers, and cloud nodes. Queries that correlate many tables create a difficult join-order problem: exact classical enumeration becomes impractical as the number of relations grows, while production optimizers must rely on bounded dynamic programming and heuristics. Recent work has encoded join ordering as Quadratic Unconstrained Binary Optimization (QUBO) and has evaluated digital annealers, quantum annealers, and hybrid quantum–classical optimizers. However, existing prototypes bind the database engine, encoding, solver interface, and deployment topology together. This work in progress proposes a platform-independent model that separates five concerns: IoT deployment, database and query structure, QUBO encoding, solver capabilities, and execution traceability. The proposal is grounded in our functional SQLite prototype, previously reported at JISBD 2026, which conditionally delegates join ordering to a private PC library compatible with Fujitsu Digital Annealer code. The intended model supports multiple database engines, QUBO formulations, algorithms, hardware classes, and locations across the IoT–edge–cloud continuum, while preserving validation and native-planner fallback. We define a preliminary conceptual metamodel, transformation chain, consistency rules, and evaluation plan; implementation and empirical validation remain future work.
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.