Self-attention enabled quantum path analysis of high-harmonic generation in solids

High-harmonic generation (HHG) in solids provides a powerful platform to probe ultrafast electron dynamics and interband–intraband coupling. However, disentangling the complex many-body contributions in the HHG spectrum remains challenging. Here we introduce a machine-learning approach based on a Transformer encoder to analyze and reconstruct HHG signals computed from a one-dimensional Kronig–Penney model. The self-attention mechanism inherently highlights correlations between temporal dipole dynamics and high-frequency spectral components, allowing us to identify signatures of nonadiabatic band coupling that are otherwise obscured in standard Fourier analysis. By combining attention maps with Gabor time–frequency analysis, we extract and amplify weak coupling channels that contribute to even-order harmonics and anomalous spectral features. Our results demonstrate that multi-head self-attention acts as a selective filter for strong-coupling events in the time domain, enabling a physics-informed interpretation of high-dimensional quantum dynamics. This work establishes Transformer-based attention as a versatile tool for solid-state strong-field physics, opening new possibilities for interpretable machine learning in attosecond spectroscopy and nonlinear photonics.