Conditional Diffusion Model for One-Shot Metasurface Design in Scalable Ion-Trap Quantum Computing

Precise beam shaping is essential for many trapped-ion quantum computing architectures, where grating couplers are the conventional solution for delivering light from a photonic chip to an ion. The required beam properties, such as a Gaussian profile with a well-controlled beam waist, pure circular polarization, and steered in a specific direction, require a sophisticated design space. We replace standard grating structures with metasurfaces consisting of subwavelength pixels, transforming the problem into a complex inverse design challenge. Here, conventional multi-objective optimization methods require extensive computational resources and must be re-run for each new target parameter. We propose a hybrid deep learning-driven approach to accelerate the design process by integrating a surrogate-assisted optimization pipeline and generative models. Our approach significantly reduces computational cost while improving flexibility in beam engineering, making it a promising candidate for scalable ion-trap integration.