Electrical Engineering Trailblazer Seminar
I am a Ph.D. candidate in the School of Electrical and Computer Engineering at Cornell University. My research interests lie at the intersection of classical and quantum information theory, privacy, and statistical learning. I received my B.Sc. in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka, and M.Sc. from Cornell University, USA, in 2018 and 2023, respectively. Before joining Cornell in Spring 2021, I was a lecturer at the University of Moratuwa. I also served as a research intern at the Singapore University of Technology and Design in 2017. Since May 2024, I have been a Graduate TA Development Consultant in the College of Engineering. I am the recipient of a Cornell ECE Fellowship (2021), an Honorable Mention in the Jane Street Graduate Fellowship (2024), and travel grants to attend Beyond IID, ISIT, and NASIT. In conjunction with my academic interests, I'm also passionate about outreach, mentoring, and volunteering.
Abstract: Quantum hypothesis testing (QHT) has been extensively studied from an information-theoretic perspective, focusing on the optimal decay rate of error probabilities based on the number of samples of an unknown state. First, we examine the sample complexity of QHT, wherein the goal is to determine the minimum samples needed to reach a desired error probability. We show that the sample complexity of symmetric binary QHT depends logarithmically on the inverse error probability and inversely on the negative logarithm of the fidelity. Next, we explore QHT under privacy constraints, quantified by quantum local differential privacy. We develop upper bounds on the contraction coefficients of quantum divergences under privacy constraints, including the hockey-stick divergence, and fully characterize the contraction coefficient for the trace distance. Finally, we derive bounds on the sample complexity of private QHT and identify cases where these bounds are tight.
Joint works with Hao-Chung Cheng, Nilanjana Datta, Nana Liu, Robert Salzmann, and Mark
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