@article{TEXTUAL,
      recid = {11498},
      author = {Chen, Senrui and Yu, Wenjun and Zeng, Pei and Flammia,  Steven T.},
      title = {Robust Shadow Estimation},
      journal = {PRX Quantum},
      address = {2021-09-22},
      number = {TEXTUAL},
      abstract = {Efficiently estimating properties of large and strongly  coupled quantum systems is a central focus in many-body  physics and quantum information theory. While quantum  computers promise speedups for many of these tasks,  near-term devices are prone to noise that will generally  reduce the accuracy of such estimates. Here, we propose a  sample-efficient and noise-resilient protocol for learning  properties of quantum states building on the shadow  estimation scheme [Huang et al., Nature Physics 16,  1050-1057 (2020)]. By introducing an experimentally  friendly calibration procedure, our protocol can  efficiently characterize and mitigate noises in the shadow  estimation scheme, given only minimal assumptions on the  experimental conditions. When the strength of noises can be  bounded, our protocol approximately retains the same order  of sample efficiency as the standard shadow estimation  scheme, while also possessing a provable noise resilience.  We give rigorous bounds on the sample complexity of our  protocol and demonstrate its performance with several  numerical experiments, including estimations of quantum  fidelity, correlation functions and energy expectations,  etc., which highlight a wide spectrum of potential  applications of our protocol on near-term devices.},
      url = {http://knowledge.uchicago.edu/record/11498},
}