Automatic Index Tuning via Quantum Deep Reinforcement Learning

Jorge Duarte, Le Gruenwald, Laurent D’Orazio, Jorge Bernardino

The Index Selection Problem (ISP) refers to the task of automatically identifying the most appropriate set of indexes for a given database workload that can minimize execution costs. However, ISP is a fundamental yet complex challenge in database management systems. In the era of data-intensive applications, efficient index strategies are increasingly necessary to maintain scalability and responsiveness. This paper presents a novel automated index selection algorithm for centralized databases that employs a Double Deep Q-Network (DDQN) as the classical learning backbone and extends it with quantum-enhanced variants. Two hybrid quantum variants were proposed: Quantum Double Deep Q-Network Mixed (QDDQNM), which incorporates a residual classical pathway, and Quantum Double Deep Q-Network Boosted (QDDQNB), a boosted model without residuals. All variants were systematically evaluated using the TPC-H benchmark at two small scale factors, 10 MB and 100 MB. Experimental results show that the evaluated Deep Reinforcement Learning (DRL)-based methods improve on the SMARTIX baseline within this proof-of-concept setting. The quantum-enhanced models achieved higher best-run accumulated rewards in the reported experiments, but they also incurred substantially higher simulation cost. The results therefore suggest interesting hybrid learning behavior under the tested conditions, while also highlighting that practical scalability and cost-performance trade-offs remain important limitations for future work.