Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.10623 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917735813873664 |
|---|---|
| author | 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. |
| author_facet | 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. |
| 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10623 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing 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. Quantum Physics Systems and Control 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. |
| title | Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing |
| topic | Quantum Physics Systems and Control |
| url | https://arxiv.org/abs/2401.10623 |