# QRLIT: Quantum Reinforcement Learning for Database Index Tuning
###### 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.  
database; indexing; quantum computing; quantum reinforcement learning; Grover's search +
Footnote †: journal:  
0000-0001-9991-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-99999-9999-99999-99999-99999-9999-99999-99999-99999-99  
and by a quantum search algorithm proposed by [PERSON] [18], with the ability to perform searches on unstructured data with complexity \(\mathrm{O}\left(\sqrt{\mathrm{N}}\right)\), where \(\mathrm{N}\) is the number of elements in the search space. Additionally, in database management, quantum computing has been investigated with promising results in several areas of database management, including query optimization and transaction scheduling [19], which are two other NP-hard problems.  
However, an analysis of the current state of the art reveals that there are no studies that implement quantum reinforcement learning strategies with experimental results in the process of automating index tuning. To address this gap, in this paper, an existing index tuning algorithm for classical computers is implemented and a quantum-classical (hybrid) version, called Quantum Reinforcement Learning for database Index Tuning (QRLIT), that employs the capabilities of [PERSON]'s search is proposed. The primary objective is to compare the performance of the hybrid algorithm against its classical counterpart.  
The implemented classical algorithm [3; 20] employs a machine learning technique called reinforcement learning [21]. It is composed of two principal elements, a designated agent and environment. The agent learns to make decisions through interactions with the environment using a trial-and-error learning method. The classical index tuning algorithm employs a technique called Epsilon-greedy [21] to balance the agent's ability to explore or follow its learned policy (exploiting). The proposed hybrid model replaces this technique with [PERSON]'s search algorithm, which enables a probabilistic approach and a natural balancing of the exploring-exploiting duality through the manipulation of the number of iterations.  
This paper contributes a novel algorithm that combines quantum computing with reinforcement learning to automate the process of database index tuning. Furthermore, a series of experiments demonstrate the advantages of using quantum computing over traditional systems. The results obtained indicate that QRLIT converges faster to an optimal policy and is able to produce a higher reward in terms of queries processed per hour than its classical counterpart.  
The rest of the paper is organized as follows: Section 2 provides some background information. Section 3 presents an overview of the state of the art, and the classical index tuning algorithm with its quantum counterpart implementation is described in Section 4. The experimental environment and results obtained from running both algorithms are detailed in Section 5. Finally, Section 6 concludes the paper and proposes directions for future work.