Author

Pia Siegl

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Review Sep 2026

Quantum Algorithms for Computational Fluid Dynamics

We present a comprehensive review of quantum approaches for solving partial differential equations (PDEs) arising in computational fluid dynamics (CFD). We examine fully quantum approaches, including quantum linear system algorithms (QLSAs), ranging from the Harrow--Hassidim--Lloyd (HHL) algorithm to quantum singular v...

Mario Guillaume Cecile, Nis-Luca van Hulst, Tomohiro Hashizume et al. · 0 citations

Preprint Aug 2026

Efficient Treatment of Non-Linearity in Quantum Computational Fluid Dynamics Using Hybrid Tensor Networks

This work presents a hybrid quantum-classical tensor network algorithm that addresses this bottleneck by combining variational time-stepping with quantum tensor programming to efficiently compile operators and time-dependent fields into quantum circuits.

Pia Siegl, Nis-Luca van Hülst, Maximilian Mandelt Buxad'e et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions

Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise ev...

Xiao Wang, Tomohiro Hashizume, Pia Siegl et al. · 2 citations