On linearity or non-linearity in machine learning for quantum chaotic dynamics
The study tested two machine learning models, a complex Transformer and a simple linear model (DLinear), to predict quantum dynamics in chaotic systems using a few-qubit PXP chain as a test case. The Transformer performed well only in ergodic regimes but failed near quantum many-body scars. DLinear showed stable, accurate predictions across all tested initial states, suggesting that complex quantum evolution can be accurately forecasted by simple linear mappings.