Abstract
Classical shadows enable remarkably efficient estimation of quantum observables, yet their connection to conventional techniques is unclear. In simulated examples we show that Bayesian mean estimation attains lower error on average, whereas classical shadows excel for specific states of interest.
| Original language | English |
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| Title of host publication | 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781943580910 |
| State | Published - May 2021 |
| Event | 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Virtual, Online, United States Duration: May 9 2021 → May 14 2021 |
Publication series
| Name | 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings |
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Conference
| Conference | 2021 Conference on Lasers and Electro-Optics, CLEO 2021 |
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| Country/Territory | United States |
| City | Virtual, Online |
| Period | 05/9/21 → 05/14/21 |
Funding
Acknowledgments.—This work was funded by the U.S. Department of Energy, Office of Advanced Scientific Computing Research, through the Quantum Algorithm Teams and Early Career Research Programs. This work was performed in part at Oak Ridge National Laboratory, operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC05-00OR22725. This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).