@INPROCEEDINGS {Bird2026qDINA,
author = { Sam Bird and Le Gruenwald and Sven Groppe },
booktitle = { 2026 IEEE 42nd International Conference on Data Engineering Workshops (ICDEW) },
title = {{ A Divergent Index Tuning Advisor Using Quantum Machine Learning for Distributed Databases }},
year = {2026},
volume = {},
ISSN = {},
pages = {1-7},
abstract = { Automatic index selection is a critical task for optimising query performance in distributed database systems. This problem is NP-hard, and existing approaches based on heuristic methods or optimisation problems struggle to generate good recommendations within a reasonable timeframe for large-scale database applications. While using classical deep reinforcement learning with Deep Q-Networks (DQN) has shown promise, its scalability in high-dimensional state spaces remains a challenge. In this paper, we explore the application of hybrid quantum-classical deep reinforcement learning, specifically a Quantum Deep Q-Network (Q-DQN), to the divergent index tuning problem. Using the TPC-H benchmark on a varying number of database replicas, we compare the performance of classical divergent index tuning against a hybrid quantum-classical algorithm using a Q-DQN implemented on the IBM Qiskit Aer simulator. We find that the hybrid quantum-classical algorithm exhibits remarkably higher sample efficiency, converging in significantly fewer action steps while maintaining a comparable query execution time. Overall, our findings suggest that variational quantum circuits provide an effective representation for deep reinforcement learning, achieving faster policy convergence, and that when appropriately configured, this improved sample efficiency can be obtained without loss in query execution performance. },
keywords = {index selection;replicated database;machine learning;quantum computing},
doi = {10.1109/ICDEW71238.2026.00005},
url = {https://doi.ieeecomputersociety.org/10.1109/ICDEW71238.2026.00005},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = May}