Journal Article

FZJ-2025-02069

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png QUASIM: Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing

Maass, W. (Corresponding author) ; Agrawal, A. ; Ciani, A.FZJ* ; Danz, S.FZJ* ; Delgadillo, A. ; Ganser, P. ; Kienast, P. ; Kulig, M. ; König, V. ; Rodellas-Gràcia, N.FZJ* ; Rughubar, R.FZJ* ; Schröder, S. ; Stautner, M. ; Stein, H. ; Stollenwerk, T.FZJ* ; Zeuch, D. ; Wilhelm-Mauch, F.FZJ*

2024
Springer Berlin

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Please use a persistent id in citations: doi:10.1007/s13218-024-00860-x  doi:10.34734/FZJ-2025-02069

Abstract: 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 analyze 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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Contributing Institute(s):

1. Quantum Computing Analytics (PGI-12)

Research Program(s):

1. 5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522) (POF4-522)

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; DEAL Springer ; Emerging Sources Citation Index ; IF < 5 ; JCR ; SCOPUS ; Web of Science Core Collection

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