3 citations · 6 across the 4 of their papers we have counts for
5 papers
Quantum Algorithms for Computational Fluid Dynamics
Mario Guillaume Cecile, Nis-Luca van Hülst, Tomohiro Hashizume +19
We present a comprehensive review of quantum approaches for solving partial differential equations (PDEs) arising in computational fluid dynamics (CFD). We examine fully quantum ap…
Efficient Treatment of Non-Linearity in Quantum Computational Fluid Dynamics Using Hybrid Tensor Networks
Pia Siegl, Nis-Luca van Hülst, Maximilian Mandelt Buxadé +2
Nonlinear terms present a fundamental challenge for quantum computational fluid dynamics, as their implementation on inherently linear quantum hardware typically requires resource-…
Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions
Xiao Wang, Tomohiro Hashizume, Pia Siegl +1
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 nonlin…
Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates
Nis-Luca van Hülst, Pia Siegl, Paul Over +5
We introduce an algorithmic framework based on tensor networks for computing fluid flows around immersed objects in curvilinear coordinates. We show that the tensor network simulat…
Tensor-Programmable Quantum Circuits for Solving Differential Equations
Pia Siegl, Greta Sophie Reese, Tomohiro Hashizume +2
We present a quantum solver for partial differential equations based on a flexible matrix product operator representation. Utilizing mid-circuit measurements and a state-dependent…