Linear DLinear forecasts PXP-chain observables stably across the whole initial-state family, while the nonlinear Transformer degrades progressively toward the Néel (scarred) limit
The work casts quantum many-body dynamics as time-series forecasting on a few-qubit PXP chain realizable with Rydberg-atom arrays, training a nonlinear Transformer and a linear DLinear with the same composite loss to predict local magnetizations and connected two-point correlators from a history window; the Transformer predicts accurately in the more ergodic regime but deteriorates progressively as the initial state approaches the scarred limit, whereas DLinear stays accurate across the entire family of initial states, its main deviations being small high-frequency oscillations with little effect on overall prediction error.