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Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware

  • The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for the known quantum algorithms. We present the first quantum implementation of join ordering, which is one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes that can be processed with current quantum annealers. However, we also confirm that limits of early-stage technology are quickly reached. Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future quantum computers, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and possible problem size. In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.

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Metadaten
Author:Manuel SchönbergerORCiD, Stefanie ScherzingerORCiD, Wolfgang MauererORCiDGND
URN:urn:nbn:de:bvb:898-opus4-56634
DOI:https://doi.org/10.1145/3588946
Parent Title (English):Proceedings of the ACM on Management of Data, PACMMOD
Publisher:ACM
Place of publication:New York, NY,
Document Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2022/12/28
Tag:Hardware; Informations systems; Join algorithms; Quantum cmputation
Volume:1
Issue:1
Article Number:92
First Page:1
Last Page:27
Note:
Corresponding author: Manuel Schönberger
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Labor für Digitalisierung (LFD)
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Corresponding author der OTH Regensburg
Funding:Publikationsfonds der OTH Regensburg
Licence (German):Creative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell - Weitergabe unter gleichen Bedingungen 4.0 International