A new LCNU-to-LCU decomposition enables a generalized quantum framework for Carleman-linearized polynomial systems like the lattice Boltzmann equation, with Ns scaling as O(α² Q²) independent of spatial and temporal discretization points.
A Linear Combination of Unitaries Decomposition for the Laplace Operator
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D-VQLS with FWHT Pauli decomposition and 1% thresholding reduces circuit evaluations by 256x for 10-qubit tridiagonal systems while achieving over 99.99% fidelity and near-ideal scaling on up to 96 GPUs.
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Quantum Data Loading for Carleman Linearized Systems: Application to the Lattice-Boltzmann Equation
A new LCNU-to-LCU decomposition enables a generalized quantum framework for Carleman-linearized polynomial systems like the lattice Boltzmann equation, with Ns scaling as O(α² Q²) independent of spatial and temporal discretization points.
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Distributed Variational Quantum Linear Solver
D-VQLS with FWHT Pauli decomposition and 1% thresholding reduces circuit evaluations by 256x for 10-qubit tridiagonal systems while achieving over 99.99% fidelity and near-ideal scaling on up to 96 GPUs.