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Bibliographic Details
Main Authors:	Maass, Wolfgang, Agrawal, Ankit, Ciani, Alessandro, Danz, Sven, Delgadillo, Alejandro, Ganser, Philipp, Kienast, Pascal, Kulig, Marco, König, Valentina, Rodellas-Gràcia, Nil, Rughubar, Rivan, Schröder, Stefan, Stautner, Marc, Stein, Hannah, Stollenwerk, Tobias, Zeuch, Daniel, Wilhelm, Frank K.
Format:	Preprint
Published:	2024
Subjects:	Quantum Physics Systems and Control
Online Access:	https://arxiv.org/abs/2401.10623
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Table of Contents:

Quantum computing (QC) and machine learning (ML), taken individually or combined into quantum-assisted ML (QML), are ascending computing paradigms whose calculations come with huge potential for speedup, increase in precision, and resource reductions. Likely improvements for numerical simulations in engineering imply the possibility of a strong economic impact on the manufacturing industry. In this project report, we propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing, consisting of various layers ranging from hardware to algorithms to service and organizational layers. In addition, we give insight into the current state of the art of applications research based on QC and QML, both from a scientific and an industrial point of view. We further analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.

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