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Cited by 310

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Practical Characterization of Quantum Devices without Tomography

Open access
da Silva, Marcus P., Landon-Cardinal, Olivier, Poulin, David·2011·Physical Review Letters

Abstract

Quantum tomography is the main method used to assess the quality of quantum information processing devices. However, the amount of resources needed for quantum tomography is exponential in the device size. Part of the problem is that tomography generates much more information than is usually sought. Taking a more targeted approach, we develop schemes that enable (i) estimating the fidelity of an experiment to a theoretical ideal description, (ii) learning which description within a reduced subset best matches the experimental data. Both these approaches yield a significant reduction in resources compared to tomography. In particular, we demonstrate that fidelity can be estimated from a number of simple experiments that is independent of the system size, removing an important roadblock for the experimental study of larger quantum information processing units.

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Metadata

Authors
da Silva, M. P., Landon-Cardinal, O., & Poulin, D.
Venue
Physical Review Letters
Published
2011-11-16
Volume
107
Issue
21
Pages
210404-210404
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W2072878982
Cited by
310
References
49
FWCI
14.78
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Practical Characterization of Quantum Devices without Tomography. https://app.cochat.ai/papers/10.1103/physrevlett.107.210404
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da Silva, M. P., Landon-Cardinal, O., & Poulin, D. (2011). Practical Characterization of Quantum Devices without Tomography. Physical Review Letters. https://doi.org/10.1103/physrevlett.107.210404

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.