In-Person: West Hall 411
Zoom: https://umich.zoom.us/j/98748463202?jst=2
Abstract:
The pursuit of quantum advantage in solving large-scale computational problems is often seen as a shining treasure. Achieving this goal, however, requires the accurate realization of smaller-scale quantum gates and control operations. Understanding and characterizing modular gate and control errors is therefore essential for building reliable quantum applications. Earlier work has typically pursued either universal algorithms with theoretical guarantees or black-box engineering approaches with no guarantees. Yet, problem-specific structures offer opportunities for efficient and robust system characterization at the intersection of theory and practice. In this talk, I will present how structured signal transformation and processing can be used to exploit such structures. I will first introduce a gate characterization method that is both resource-efficient and robust against complex experimental errors, drawing parallels to parameter estimation in classical statistics. I will then generalize this idea to functional signals and present a novel non-parametric estimation paradigm.
Bio:
Yulong Dong is an Assistant Professor in ECE, with a courtesy appointment in Mathematics, at the University of Michigan. He earned his Ph.D. in Applied Mathematics from UC Berkeley in 2023. Before joining UMich, he worked as a research intern at Google Quantum AI, then as a research scientist at ByteDance AI Lab in California, and subsequently at the University of Washington. His research focuses on numerical analysis, optimization, and quantum computing, with particular emphasis on quantum algorithms for scientific computing and high-precision quantum learning and sensing. His work not only provides rigorous theoretical results but also maintains close connections to practical applications. More broadly, his research aims to bridge quantum computing with applied mathematics and information theory by addressing challenging problems in quantum algorithms and sensing from numerical-analysis and information-theoretic perspectives.
