Refreshments: 10:30 AM

Seminar: 11:00 AM

Title: A Statistical Framework for Benchmarking Quantum Computers

Abstract: Recent years have witnessed quantum computing technologies increasingly move from theoretical proposals to functioning experimental platforms, reaching major milestones such as the demonstration of beyond-classical computational tasks. Despite these exciting advances, current quantum computers experience hardware-level errors which limit their scalability, and which must be carefully identified before they can be mitigated. In this talk, I will develop a statistical framework for characterizing errors in quantum devices, using an existing experimental platform known as random circuit sampling. Data arising from this experiment can be described through a high-dimensional discrete latent variable model parametrized by the device's error rates. We develop estimators for these error rates which are provably consistent even for large-scale quantum devices. We then apply our methods to benchmark a recent state-of-the-art quantum processor. 

Biography: I am a Norbert Wiener postdoctoral associate in the Statistics and Data Science Center at the Massachusetts Institute of Technology (MIT). I completed my PhD in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), where I was advised by Sivaraman Balakrishnan and Larry Wasserman. Before joining CMU, I received a Bachelor of Science in Mathematics from McGill University, where I was mentored by Abbas Khalili.

I am broadly interested in nonparametric statistics and statistical machine learning. My theoretical research centers around statistical optimal transport, latent variable models, nonparametric hypothesis testing, and distribution-free inference. Much of my recent work is motivated by interdisciplinary collaborations in the physical sciences, particularly in quantum computing and high energy physics.