QRLIT: Quantum Reinforcement Learning for Database Index Tuning

Diogo Barbosa, Le Gruenwald, Laurent D’Orazio and Jorge Bernardino ()
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Diogo Barbosa: Institute of Engineering of Coimbra—ISEC, Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal
Le Gruenwald: School of Computer Science, The University of Oklahoma, Norman, OK 73019, USA
Laurent D’Orazio: IRISA, CNRS, University of Rennes, Rue de Kerampont, 22305 Lannion Cedex, France
Jorge Bernardino: Institute of Engineering of Coimbra—ISEC, Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal

Future Internet, 2024, vol. 16, issue 12, 1-17

Abstract: Selecting indexes capable of reducing the cost of query processing in database systems is a challenging task, especially in large-scale applications. Quantum computing has been investigated with promising results in areas related to database management, such as query optimization, transaction scheduling, and index tuning. Promising results have also been seen when reinforcement learning is applied for database tuning in classical computing. However, there is no existing research with implementation details and experiment results for index tuning that takes advantage of both quantum computing and reinforcement learning. This paper proposes a new algorithm called QRLIT that uses the power of quantum computing and reinforcement learning for database index tuning. Experiments using the database TPC-H benchmark show that QRLIT exhibits superior performance and a faster convergence compared to its classical counterpart.

Keywords: database; indexing; quantum computing; quantum reinforcement learning; Grover’s search (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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