Overview of Learning and Testing Stabilizer States
Abstract: In this talk, I will briefly overview some recent works on learning and testing stabilizer states. In the case of learning, the goal is, given copies of an unknown stabilizer state, the learner should exactly identity the unknown stabilizer state and in the case of testing, the goal is, given copies of an unknown state decide if the unknown state is close or far from the class of stabilizer states. We have polynomial-time algorithms for both tasks, and I will discuss these results in this talk.
Bio: Srinivasan has been a Senior Research Scientist at IBM T. J. Watson Research Center since 2020. Before this, I was a Postdoctoral Researcher at the Center for Theoretical Physics, MIT. I received my Ph.D. in 2018 from Centrum Wiskune & Informatica and QuSoft, Amsterdam, Netherlands, supervised by Ronald de Wolf. Before that, I finished my M.Math in Mathematics from the University of Waterloo and the Institute of Quantum computing, Canada, 2014, supervised by Michele Mosca.
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