Tudor Manole (MIT): Statistics and DSI Joint Colloquium

Please join us for a Statistics and DSI joint colloquium.

Monday, January 12
11:30am – 12:30pm
Data Science Institute, Room 105
5460 S University Ave, Chicago, IL 60615

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, obtaining a detailed report of error rates which were largely unavailable from past studies. I will close by placing these results in the broader context of my interdisciplinary work in the physical sciences, and by discussing some of my other research interests in nonparametric statistics and statistical optimal transport.

Bio: 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.