Machine Learning Inverse Design of Topological Quantum States in Photonic Topological Insulators

Topological quantum optics has emerged as a promising platform for realizing robust quantum states due to its intrinsic topological protection. However, the complexity of band topologies presents significant challenges in designing devices capable of exciting tailored quantum states for specific applications. Here, we propose a machine learning-based inverse design approach for generating topological quantum states in photonic topological insulators. In our method, these quantum states are generated via four-wave mixing processes achieved through precise dispersion engineering of topological edge states. Our approach incorporates a tandem neural network architecture to facilitate the inverse design of kagome lattice structures within photonic crystals, using the Schmidt number as a critical performance metric. Moreover, we demonstrate that our deep learning framework effectively optimizes high-purity topological single-photon sources. This strategy offers a versatile and generalized method for tailoring topological quantum states, presenting significant potential to advance quantum information processing.