A quantum machine learning surrogate based on parameterized circuits with data re-uploading approximates the full BGK collision dynamics in LBM across all admissible relaxation parameters and is validated on Taylor-Green vortex and double shear layer benchmarks.
Carleman, Application de la théorie des équations intégrales linéaires aux systèmes d’équations différentielles non linéaires, Acta Mathematica 59 (0) (1932) 63–87
3 Pith papers cite this work. Polarity classification is still indexing.
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The reduced basis algorithm exactly reproduces the nonlinear dynamics of polynomial ODEs and PDEs over m timesteps using a linear quantum operator on a reduced monomial basis, with qubit scaling logarithmic in grid size for PDEs.
Demonstration of quantum circuit implementation for 2D obstacle flow via Carleman-linearized LBM solved with QSVT, achieving logarithmic qubit and gate scaling with lattice points.
citing papers explorer
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A Quantum-Classical Surrogate Model for the Collision Operator of the Lattice Boltzmann Method
A quantum machine learning surrogate based on parameterized circuits with data re-uploading approximates the full BGK collision dynamics in LBM across all admissible relaxation parameters and is validated on Taylor-Green vortex and double shear layer benchmarks.
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A Demonstration of Quantum Circuit Implementation for Obstacle Flow Using Carleman-Linearized Lattice Boltzmann Method
Demonstration of quantum circuit implementation for 2D obstacle flow via Carleman-linearized LBM solved with QSVT, achieving logarithmic qubit and gate scaling with lattice points.