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As a two-dimensional planar material with low depth profile, a metasurfacecan generate non-classical phase distributions for the transmitted andreflected electromagnetic waves at its interface. Thus, it offers moreflexibility to control the wave front. A traditional metasurface design processmainly adopts the forward prediction algorithm, such as Finite Difference TimeDomain, combined with manual parameter optimization. However, such methods aretime-consuming, and it is difficult to keep the practical meta-atom spectrumbeing consistent with the ideal one. In addition, since the periodic boundarycondition is used in the meta-atom design process, while the aperiodiccondition is used in the array simulation, the coupling between neighboringmeta-atoms leads to inevitable inaccuracy. In this review, representativeintelligent methods for metasurface design are introduced and discussed,including machine learning, physics-information neural network, and topologyoptimization method. We elaborate on the principle of each approach, analyzetheir advantages and limitations, and discuss their potential applications. Wealso summarise recent advances in enabled metasurfaces for quantum opticsapplications. In short, this paper highlights a promising direction forintelligent metasurface designs and applications for future quantum opticsresearch and serves as an up-to-date reference for researchers in themetasurface and metamaterial fields.

Recent Advances in Metasurface Design and Quantum Optics Applications  with Machine Learning, Physics-Informed Neural Networks, and Topology  Optimization Methods