Statistical analysis of randomized benchmarking
Abstract
Randomized benchmarking and variants thereof, which we collectively call RB+, are widely used to characterize the performance of quantum computers because they are simple, scalable, and robust to state-preparation and measurement errors. However, experimental implementations of RB+ allocate resources suboptimally and make ad-hoc assumptions that undermine the reliability of the data analysis. In this paper, we propose a simple modification of RB+ which rigorously eliminates a nuisance parameter and simplifies the experimental design. We then show that, with this modification and specific experimental choices, RB+ efficiently provides estimates of error rates with multiplicative precision. Finally, we provide a simplified rigorous method for obtaining credible regions for parameters of interest and a heuristic approximation for these intervals that performs well in currently relevant regimes.
Cite
@article{arxiv.1901.00535,
title = {Statistical analysis of randomized benchmarking},
author = {Robin Harper and Ian Hincks and Chris Ferrie and Steven T. Flammia and Joel J. Wallman},
journal= {arXiv preprint arXiv:1901.00535},
year = {2019}
}
Comments
9 pages, 2 figures