@phdthesis{Schoenberger, author = {Sch{\"o}nberger, Manuel}, title = {Eine zweigleisige Strategie hin zu ausgereiftem Quanten-Datenmanagement}, doi = {10.35096/othr/pub-8477}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-84774}, school = {Ostbayerische Technische Hochschule Regensburg}, pages = {xvi, 167}, abstract = {By relying on stagnant general-purpose devices limited by the end of Moore's law, conventional data management (DM) methods are challenged by ever-increasing computational loads. As a promising alternative, we may rely on quantum processing units (QPU) as special-purpose accelerators, which may overcome the limitations of conventional systems by exploiting quantum phenomena. However, given their early-stage, prototypical nature, contemporary QPUs remain limited in size and reliability, which renders them unfit for practical scenarios. In this thesis, we therefore propose a twofold strategy to aid the shift towards mature quantum data management. As a first fundamental pillar, our strategy rests on QPU-DM co-design: Inspired by the conventional hardware-software co-design paradigm, our method yields recommendations for the tailored design of QPU architecture, to craft quantum systems conforming to the specific requirements of DM tasks. Based on an empirical assessment involving contemporary quantum systems, and simulating enhanced QPU architectures, our method identifies the most effective among a variety of architectural enhancements, and allows us to derive recommendations on the design of QPUs tailored to DM tasks. However, to decide whether QPU-DM co-design efforts are truly worth pursuing, we require insights into the true potential of quantum systems for DM up-front. As a second fundamental pillar, our strategy hence assesses the algorithmic aptness of our methods on quantum-inspired special-purpose hardware (HW): While classical in nature, such systems mimic the workings of pure QPUs, and thus provide a lower bound on the capabilities of future QPUs. Thereby, our strategy yields insights into the true potential of future QPUs, which are expected to achieve still higher performance upon obtaining a sufficient level of maturity. We demonstrate the aptness of our twofold strategy on fundamental DM and query optimisation tasks by (1) deriving QPU design recommendations for the join ordering (JO) problem by applying our QPU-DM co-design method, (2) demonstrating the scalability aptness of quantum-inspired HW for extremely large JO solution spaces that prove challenging for conventional approaches, (3) deriving methods to solve the most extensive class of JO problems on quantum(-inspired) systems, and (4) conducting a still more extensive assessment of quantum-inspired HW for multiple query optimisation. Thereby, we aid the shift from currently imperfect QPUs towards systems fit for mature quantum data management.}, language = {en} }