001041810 001__ 1041810
001041810 005__ 20250514202226.0
001041810 0247_ $$2doi$$a10.34734/FZJ-2025-02445
001041810 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02445
001041810 037__ $$aFZJ-2025-02445
001041810 041__ $$aEnglish
001041810 1001_ $$0P:(DE-HGF)0$$aChaudhary, Smit$$b0$$eCorresponding author
001041810 1112_ $$a35th Parallel CFD International Conference 2024$$cBonn$$d2024-09-02 - 2024-09-04$$gParCFD 2024$$wGermany
001041810 245__ $$aSolving Fluid Dynamics Equations with Differentiable Quantum Circuits
001041810 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2025
001041810 29510 $$aProceedings of the 35th Parallel CFD International Conference 2024
001041810 300__ $$a20 - 22
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001041810 4900_ $$aSchriften des Forschungszentrums Jülich IAS Series$$v69
001041810 520__ $$aDifferentiable quantum circuits (DQCs) are the hybrid quantum-classical alternative to Physics-Informed Neural Networks (PINNs). The latter ones have been introduced from the machine learning community to avoid the curse of dimensionality in mesh-based computational fluid dynamics (CFD) solvers, and allow for seamless inclusion of information from available data. The adoption of quantum circuits is motivated by enabling access to highly expressive feature maps, which might be key in capturing intricate solutions to selected fluid dynamics problems. In this work, we discuss the potential of DQCs and its recent extensions to address paradigmatic CFD use cases.
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001041810 7001_ $$0P:(DE-HGF)0$$aBalducci, Giorgio Tosti$$b1
001041810 7001_ $$0P:(DE-HGF)0$$aKyriienko, Oleksandr$$b2
001041810 7001_ $$0P:(DE-HGF)0$$aBarkoutsos, Panagiotis Kl.$$b3
001041810 7001_ $$0P:(DE-HGF)0$$aCardarelli, Lorenzo$$b4
001041810 7001_ $$0P:(DE-HGF)0$$aGentile, Antonio A.$$b5
001041810 770__ $$z978-3-95806-819-3
001041810 773__ $$a10.34734/FZJ-2025-02445
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