Inverse Design of Nanophotonic Structures using Diffusion Models and Graph Neural Networks: A Novel Paradigm for Photonic Quantum Computing Futures

The transition from electronic to photonic computing is a cornerstone of next-generation quantum technologies, yet the inverse design of nanophotonic structures-determining the optimal physical geometry from desired optical properties-remains a formidable computational bottleneck. Traditional numerical methods like Finite-Difference Time-Domain (FDTD) are prohibitively slow, while previous AI approaches like Generative Adversarial Networks (GANs) suffer from training instability and limited topological exploration. This paper introduces a novel, highly integrated deep learning framework that harmonizes Denoising Diffusion Probabilistic Models (DDPMs) with Graph Neural Networks (GNNs) for the first time in photonic inverse design. By representing nanophotonic metamaterials as crystal graphs and leveraging GNNs to encode topological features, our diffusion model performs targeted, physics-guided sampling to generate optimal structures with unprecedented efficiency. Our empirical simulations demonstrate that this framework reduces design time by over ۸۰% (dropping from ۱۲۰ hours to under ۲۴ hours) while achieving a ۹۵.۸% accuracy in predicting spectral responses. Beyond the engineering improvement, this paper provides a comprehensive "futures study" analyzing how this inverse design paradigm will accelerate the commercialization of photonic quantum chips and programmable photonic processors. We posit that this synergy of generative AI and topological deep learning is not merely a computational tool, but a pivotal catalyst for the Fourth Industrial Revolution in quantum computing.

کلیدواژه ها:

Inverse Design ، Denoising Diffusion Models ، Graph Neural Networks ، Nanophotonics ، Futures Studies ، Photonic Quantum Computing ، Generative AI