TY  - CONF
AU  - Chaudhary, Smit
AU  - Balducci, Giorgio Tosti
AU  - Kyriienko, Oleksandr
AU  - Barkoutsos, Panagiotis Kl.
AU  - Cardarelli, Lorenzo
AU  - Gentile, Antonio A.
TI  - Solving Fluid Dynamics Equations with Differentiable Quantum Circuits
VL  - 69
CY  - Jülich
PB  - Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
M1  - FZJ-2025-02445
T2  - Schriften des Forschungszentrums Jülich IAS Series
SP  - 20 - 22
PY  - 2025
AB  - Differentiable 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.
T2  - 35th Parallel CFD International Conference 2024
CY  - 2 Sep 2024 - 4 Sep 2024, Bonn (Germany)
Y2  - 2 Sep 2024 - 4 Sep 2024
M2  - Bonn, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.34734/FZJ-2025-02445
UR  - https://juser.fz-juelich.de/record/1041810
ER  -