Inertial Dirac-Frenkel dynamics yield well-posed parameter evolution for nonlinear parametrizations with a posteriori error bounds and improved numerical robustness.
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Projected Inverse Iteration reframes ground-state search for neural quantum states as an eigenvalue problem to deliver rapid, spectral-gap-insensitive convergence while retaining polynomial scaling.
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Dirac-Frenkel dynamics with inertia for nonlinearly parametrized solutions of evolution problems
Inertial Dirac-Frenkel dynamics yield well-posed parameter evolution for nonlinear parametrizations with a posteriori error bounds and improved numerical robustness.
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Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Projected Inverse Iteration reframes ground-state search for neural quantum states as an eigenvalue problem to deliver rapid, spectral-gap-insensitive convergence while retaining polynomial scaling.