Quantum Machine Learning for Join Order Optimization using Variational Quantum Circuits

Abstract

The optimization of queries speeds up query processing in databases. One of the most time-consuming tasks in query processing is the join operation, where the order of the joins plays a crucial role in determining the number of tuples to be processed for intermediate results, and hence, the overall processing costs. In this paper, we use a variational quantum circuit (VQC) to create a hybrid classical-quantum machine learning algorithm to predict efficient join orders by learning from past join orders. We develop an encoding of the join order problem using a low number of qubits. We show that VQCs with filtering of cross joins outperform the classical dynamic programming optimizer of PostgreSQL with a 2.7% faster execution time.