A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.
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MD-PNOP recasts parameter-induced operator differences as source terms to enable single-configuration neural operator training for extrapolation and acceleration of parametric PDE solvers.
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Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.
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MD-PNOP: Equation-Recast Neural Operators for Minimal-Data Extrapolation and PDE Solver Acceleration
MD-PNOP recasts parameter-induced operator differences as source terms to enable single-configuration neural operator training for extrapolation and acceleration of parametric PDE solvers.