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Cited by 667

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Using measurement-induced disturbance to characterize correlations as classical or quantum

Luo, Shunlong·2008·Physical Review A
DOI 10.1103/physreva.77.022301
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Abstract

In contrast to the seminal entanglement-separability paradigm widely used in quantum information theory, we introduce a quantum-classical dichotomy in order to classify and quantify statistical correlations in bipartite states. This is based on the idea that while in the classical description of nature measurements can be carried out without disturbance, in the quantum description, generic measurements often disturb the system and the disturbance can be exploited to quantify the quantumness of correlations therein. It turns out that certain separable states still possess correlations of a quantum nature and indicates that quantum correlations are more general than entanglement. The results are illustrated in the Werner states and the isotropic states, and are applied to quantify the quantum advantage of the model of quantum computation proposed by Knill and Laflamme [Phys. Rev. Lett. 81, 5672 (1998)].

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Metadata

Authors
Luo, S.
Venue
Physical Review A
Publisher
American Physical Society (APS)
Published
2008-02-01
Volume
77
Issue
2
ISSN
  • 0556-2791 (ISSN-L)
  • 1050-2947
  • 1094-1622
DOI
10.1103/physreva.77.022301
OpenAlex
W2043317809
Cited by
667
References
42
FWCI
58.83
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Using measurement-induced disturbance to characterize correlations as classical or quantum. https://app.cochat.ai/papers/10.1103/physreva.77.022301
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Luo, S. (2008). Using measurement-induced disturbance to characterize correlations as classical or quantum. Physical Review A. https://doi.org/10.1103/physreva.77.022301

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.