Abstract: 

The last two decades 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. The central challenge in the sustained development of large-scale quantum computers is the presence of hardware errors, which must be identified and quantified before they can be mitigated. In this talk, I will develop a statistical perspective on this problem of benchmarking quantum devices, using an 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 hardware-level error rates. We develop computationally efficient estimators for these error rates, which incorporate side information about the model via simulations from a reference quantum computer. These estimators achieve the information-theoretic estimation limits for this problem, implying that reliable estimation is possible even for large-scale quantum devices that evade classical computational abilities. We apply our methods to benchmark a recent state-of-the-art quantum processor, obtaining a detailed report with hundreds of error rates which were largely unavailable from past studies. I will conclude 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.