Quantum Solvers: Predictive Aeroacoustic & Aerodynamic modeling
Nis-Luca van H\"ulst, Theofanis Panagos, Greta Sophie Reese, Shahram Panahiyan, Tomohiro Hashizume
TL;DR
This paper presents quantum-inspired and hybrid quantum-classical algorithms for efficient industrial CFD simulation, demonstrating their potential in aerospace and automotive applications, as showcased in a recent challenge.
Contribution
It introduces novel quantum and hybrid algorithms specifically designed for predictive aeroacoustic and aerodynamic modeling in industrial CFD.
Findings
Successful application in the 2024 Airbus and BMW Quantum Computing Challenge
Demonstrated efficiency improvements over classical methods
Provided an archival record of the submitted solutions
Abstract
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.
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