Birck Seminar Series: Machine Learning and Quantum-Assisted Inverse Design for Next-Generation Photonics

Join Yuheng Chen and Vaishnavi Venkatacha Iyer from the Elmore Family School of Electrical and Computer Engineering as they present how machine learning (ML) provides a powerful framework for exploring high‑dimensional photonic and quantum‑optical design spaces. We introduce a unified suite of ML‑assisted inverse‑design methods that combine physics‑conditioned generative modeling, surrogate‑based latent optimization, and solver‑in‑the‑loop adjoint learning. These approaches enable fabrication‑aware device generation, hybrid quantum–classical optimization, correlation‑guided surrogate annealing, and closed‑loop RL‑based design of complex multilayer structures. Together, they form a cohesive toolkit for next‑generation photonic and quantum device design.