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Advances in intelligent systems and computing

Investigating Quantum vs. Classical Multi-Query Optimization for Data Warehouses

Amira Kerkad

Published Sep 2, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-27214-0_26

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As global demand for logistics continues to grow, improving the efficiency of warehouse operations has become increasingly important. While many processes in logistics warehouses have been automated, the receiving area still relies heavily on manual work. Therefore, optimizing this area is essential. When optimizing operations in a logistics warehouse, various problems must be considered, such as layout design and worker assignment. Previous research has typically focused on these problems individually. However, because they are closely related, jointly optimizing them can further improve overall efficiency. In this study, we focus on the receiving area and conduct joint optimization of layout design and worker assignment. Specifically, we extend and apply a black-box optimization method called FMQA, which combines Factorization Machines (FM) with Quantum Annealing (QA). By using Factorization Machine regression, we build an objective function from simulator data. We then use Quantum Annealing to minimize it and improve the receiving area. A comparison with an actual warehouse environment, using the multi-agent simulator, shows that our approach can reduce mean package processing time by up to 22.1%. This result demonstrates the effectiveness of joint optimization of layout design and worker assignment.
Abstract The warehouse layout problem (WLP), also known as warehouse slotting problem, aims to determine the assignment of products to storage locations while minimizing the total travel distance for picking operations. It is combinatorial in nature and well-known to be NP-hard. To obtain high-quality solutions to medium and large WLP instances efficiently remains a computational challenge, which has motivated our work on applying quantum optimization algorithms for this problem. We reformulate the classical quadratic assignment programming (QAP) model of the WLP to a quadratic unconstrained binary optimization (QUBO) model. Our QUBO model avoids adding slack variables and reduces additional overheads as in the typical QUBO reformulation approach, which facilitates the application of the D-Wave Leap Hybrid solver. Computational study is performed on a use case with 30 stock-keeping-units (SKUs) and 32 storage slots, for which the QUBO formulation has 960 binary decision variables. We show that the D-Wave Leap Hybrid solver finds better solution to the base scenario than the exact IBM CPLEX mixed-integer quadratic programming (MIQP) solver operating under a 60-second wall-clock limit, one-tenth of the 600-second budget allocated to CPLEX in this experimental design. A sensitivity analysis across 30 scenarios, generated by crossing five annual pick volume profiles with six co-picking matrix density levels (0.5 to 1.0), shows that the D-Wave hybrid solver outperforms CPLEX MIQP solver in 26 of 30 cases, with solution quality improvements of 0.4 to 12.1%. D-Wave’s advantage generally increases as the density of co-picking matrix decreases. These results demonstrate quantum utility for WLP and motivate further investigation of hybrid quantum-classical methods for combinatorial logistics optimization at industrial strength scales.
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.