Quantum Solvers: Predictive Aeroacoustic & Aerodynamic modeling
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908470981165056 |
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| author | van Hülst, Nis-Luca Panagos, Theofanis Reese, Greta Sophie Panahiyan, Shahram Hashizume, Tomohiro |
| author_facet | van Hülst, Nis-Luca Panagos, Theofanis Reese, Greta Sophie Panahiyan, Shahram Hashizume, Tomohiro |
| contents | This technical report presents our winning contribution to the 2024 Airbus and BMW Group Quantum Computing Challenge under the category 'Quantum Solvers'. This submission addresses efficient simulation in industrial CFD using (i) quantum-inspired algorithms and (ii) hybrid quantum-classical algorithms. We reproduce the submitted materials exactly as handed in, providing an archival record, with the sole addition of a note citing the publication that resulted from this challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Solvers: Predictive Aeroacoustic & Aerodynamic modeling van Hülst, Nis-Luca Panagos, Theofanis Reese, Greta Sophie Panahiyan, Shahram Hashizume, Tomohiro Quantum Physics This technical report presents our winning contribution to the 2024 Airbus and BMW Group Quantum Computing Challenge under the category 'Quantum Solvers'. This submission addresses efficient simulation in industrial CFD using (i) quantum-inspired algorithms and (ii) hybrid quantum-classical algorithms. We reproduce the submitted materials exactly as handed in, providing an archival record, with the sole addition of a note citing the publication that resulted from this challenge. |
| title | Quantum Solvers: Predictive Aeroacoustic & Aerodynamic modeling |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2507.21683 |