Rank-adaptive tensor decompositions enable memory-efficient dynamical simulations of Schrödinger's equation by compressing partially entangled quantum states while controlling truncation error via SVD thresholds.
An unconventional robust integrator for dynamical low-rank approximation.BIT Numerical Mathematics, 62(1):23–44, Mar 2022
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Dynamical Simulations of Schr\"odinger's Equation via Rank-Adaptive Tensor Decompositions
Rank-adaptive tensor decompositions enable memory-efficient dynamical simulations of Schrödinger's equation by compressing partially entangled quantum states while controlling truncation error via SVD thresholds.