Robust Shadow Estimation

Efficiently estimating properties of large and strongly coupled quantum systems is a central focus in many-body physics information theory. While computers promise speedups for many such tasks, near-term devices are prone to noise that will generally reduce the accuracy estimates. Here we show how mitigate errors shadow estimation protocol recently proposed by Huang, Kueng, Preskill. By adding an experimentally friendly calibration stage standard scheme, our robust algorithm can obtain unbiased estimate classical system hence extract useful sample-efficient noise-resilient manner given only minimal assumptions on experimental conditions. We give rigorous bounds sample complexity demonstrate its performance with several numerical experiments.