Huang, Hsin-Yuan (2022) Learning quantum states from their classical shadows. Nature Reviews Physics, 4 (2). Art. No. 81. ISSN 2522-5820. doi:10.1038/s42254-021-00411-5. https://resolver.caltech.edu/CaltechAUTHORS:20220111-643841600
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Abstract
In quantum mechanics, a quantum many-body system is represented by a large complex matrix whose size scales exponentially with the number of particles. This intrinsic exponential complexity empowers quantum technologies but, at the same time, it makes it practically impossible to completely characterize, or learn, a quantum many-body system even of moderate size (the current limit of quantum tomography being 40–50 qubits). This is an issue given that learning quantum systems is central to the development of quantum technologies.
| Item Type: | Article | |||||||||
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| Additional Information: | © 2022 Nature Publishing Group. Published 10 January 2022. The author declares no competing interests. | |||||||||
| Group: | Institute for Quantum Information and Matter | |||||||||
| Subject Keywords: | Information technology; Quantum information | |||||||||
| Issue or Number: | 2 | |||||||||
| DOI: | 10.1038/s42254-021-00411-5 | |||||||||
| Record Number: | CaltechAUTHORS:20220111-643841600 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20220111-643841600 | |||||||||
| Official Citation: | Huang, HY. Learning quantum states from their classical shadows. Nat Rev Phys 4, 81 (2022). https://doi.org/10.1038/s42254-021-00411-5 | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 112827 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | Tony Diaz | |||||||||
| Deposited On: | 11 Jan 2022 22:00 | |||||||||
| Last Modified: | 08 Feb 2022 17:30 |
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