Amortized Clustering Assistant Classification of Anomalous Hybrid Floquet Modes in a Periodically Driven Non‐Hermitian Lattice

ABSTRACT The interplay between Floquet periodically driving and non‐Hermiticity could bring about intriguing novel phenomena with anomalous Floquet topological phases of a finite‐size, tight‐binding lattice model. How to efficiently investigate on quasi‐energy and eigenstate distribution of a non‐Hermitian Floquet system with complicated driving protocol remains a challenging task. In this work, we define a somewhat complex driving protocol for a bipartite lattice system for the investigation. Thereafter, we introduce unsupervised learning method in order to explore distribution features of system eigenfunctions under different magnitude of system energy gain/loss. An amortized clustering strategy is adopted to perform state classification. We further introduce an algorithm‐selection mechanism that adapts to varying gain/loss strengths by choosing the most suitable clustering model based on unsupervised indices and task‐oriented separability. Proper employment of the selector enables us to reveal the categories of eigenstate distribution from abundant possible wave function distribution in two‐dimension lattice in another efficient way. In addition, our work provides a feasible methodology via machine Amortized Clustering, Periodically Driven non‐Hermitian Lattice, nomalous Hybrid Floquet Modeslearning method to assist in classification of Floquet modes.