Work

Quantum Machine Learning for Join Order Optimization using Variational Quantum Circuits

Year: 2023

Type: conference-paper

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.

Source: International Workshop on Big Data in Emergent Distributed Environments (BiDEDE)

DOI: 10.1145/3579142.3594299

Authors: Tobias Winker, Umut Çalikyilmaz, Le Gruenwald, Sven Groppe

Institutions: University of Lübeck, University of Oklahoma

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Bundesministerium für Bildung und Forschung

OpenAlex ID: W4380322754

FWCI: 4.429

Cites: 30

Cited by: 25

Related to: 10