Bio

Theshani Nuradha is an IQUIST Postdoctoral Scholar at the Illinois Quantum Information Science Technology Center and J.L. Doob Research Assistant Professor (Postdoctoral Faculty) in the Department of Mathematics, University of Illinois Urbana-Champaign, where she is mentored by Prof. Felix Leditzky. She earned her Ph.D. in Electrical and Computer Engineering from Cornell University in 2025, where she was advised by Prof. Mark Wilde and Prof. Ziv Goldfeld. She received her B.Sc. in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka, and M.Sc. from Cornell University in 2018 and 2023, respectively. Her research interests include quantum information theory, privacy, and statistical learning. She was a Graduate TA Development Consultant in the College of Engineering at Cornell University in 2024/25. She was recognized as a Trailblazing Young Researcher by Caltech in 2024 and by the Robert Mozia Distinguished Service Award from the College of Engineering at Cornell University in 2025.

Theory of Private and Noisy Information Processing in a Quantum World

The rapid growth of information technologies and diverse forms of data presents new challenges in analyzing complex systems. With the rise of quantum technologies and hybrid classical-quantum systems, it is essential to understand their capabilities, limitations, and privacy implications. My research focuses on the performance limits of such systems through mathematical modeling, trade-off quantification, and the development of new analytical tools. My research agenda centers on three overarching goals: (1) designing provably private quantum and hybrid systems; (2) understanding the impact of resource constraints in quantum information-processing tasks; and (3) developing quantum information measures tailored to modern applications.

First, with the growing interest in quantum and hybrid classical-quantum systems, ensuring the privacy of quantum data is crucial. While Quantum Differential Privacy (QDP) was introduced to protect quantum states, its applicability is limited by its inability to incorporate physical constraints. To address this, I developed Quantum Pufferfish Privacy (QPP), a flexible framework that could lay a foundation for privacy-preserving learning in quantum systems.

Second, quantum Shannon theory assumes infinite resources, yet real-world quantum devices operate with finite resources and noisy operations. I investigated how many samples of noiseless, noisy, or private quantum data are needed to guess a state among multiple possibilities within a given error tolerance. This quantification informs quantum simulation and learning theory, while offering insights into the limitations of noisy quantum computing and methods to address them.

Thirdly, emerging technologies demand new information measures suited to quantum and hybrid contexts. I introduced multivariate fidelities to assess similarity among multiple quantum states and developed smooth divergences to study resource-distillation rates for impure states.

Through these efforts, my research aims to build a theoretical foundation for privacy, resource efficiency, and analytical tools in quantum and hybrid systems, bridging rigorous theory with practical relevance for next-generation quantum technologies.