Upcoming Talks

Photons offer a versatile platform for information processing, from long-distance quantum communication to scalable computing and machine learning. However, realizing distributed photonic information processing requires advances not only in individual optical devices, but also in network protocols, computing architectures, and system-level co-design. In this talk, I will present our research toward scalable and distributed photonic information processing. I will first discuss the physical infrastructure and protocols for large-scale quantum networks, including vacuum beam guides and efficient peer-to-peer entanglement distribution. I will then introduce our work on resource-efficient photonic quantum computing architectures. Finally, I will discuss our recent efforts in privacy-preserving optical inference and distributed machine learning, exploring how shared photonic computing resources can support secure and personalized computation with minimal client-side requirements. Together, these efforts illustrate a full-stack approach to building scalable, efficient, and secure photonic information processing systems.

Yuexun Huang is a Ph.D. candidate in Quantum Engineering at the University of Chicago, advised by Prof. Liang Jiang. His research focuses on photonic information processing, spanning quantum optics, distributed quantum networks, photonic computing architectures, and privacy-preserving machine learning. His work has appeared in Physical Review Letters and ACM SIGMETRICS, with additional research contributions accepted at ISCA and MICRO. He received his B.S. in Physics from the University of Science and Technology of China.