Author

Pia Siegl

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Review Sep 2026

Quantum Algorithms for Computational Fluid Dynamics

This analysis highlights that potential quantum advantage is highly problem dependent and governed by condition number, representational complexity, state preparation, and measurement constraints, and concludes that potential quantum advantage is highly problem dependent and governed by condition number, representation...

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