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.
Low-rank tree tensor network operators for long- range pairwise interactions.SIAM Journal on Scientific Computing, 47(4):A2248–A2271
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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.