Tensor-network fractional-step method simulates incompressible flows in curvilinear coordinates with up to 20x field compression and 1000x operator compression while keeping errors below 0.3% versus finite differences.
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PODR precomputes a proper orthogonal decomposition basis from classical solutions to project quantum states onto a minimal set of coefficients for reconstruction, reducing measurements in online quantum simulations.
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Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates
Tensor-network fractional-step method simulates incompressible flows in curvilinear coordinates with up to 20x field compression and 1000x operator compression while keeping errors below 0.3% versus finite differences.
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Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition
PODR precomputes a proper orthogonal decomposition basis from classical solutions to project quantum states onto a minimal set of coefficients for reconstruction, reducing measurements in online quantum simulations.