The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Using Entanglement Improves the Precision of Quantum Measurements
Open accessAbstract
We show how entanglement can be used to improve the estimation of an unknown transformation. Using entanglement is always of benefit in improving either the precision or the stability of the measurement. Examples relevant for applications are illustrated, for either qubits or continuous variables.
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Citations
Cites
Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?
Einstein, A., Podolsky, B., & Rosen, N. (1935) · Physical Review · doi:10.1103/physrev.47.777 · Cited by 16,654
Quantum Mechanics Helps in Searching for a Needle in a Haystack
Grover, L. K. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.325 · Cited by 4,306
Probabilistic and Statistical Aspects of Quantum Theory
Holevo, A. (2011) · doi:10.1007/978-88-7642-378-9 · Cited by 2,582
Peres-Horodecki Separability Criterion for Continuous Variable Systems
Simon, R. (2000) · Physical Review Letters · doi:10.1103/physrevlett.84.2726 · Cited by 2,245
Cited by
Squeezed states of light and their applications in laser interferometers
Schnabel, R. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.04.001 · Cited by 513
Sequential feedback scheme outperforms the parallel scheme for Hamiltonian parameter estimation
Yuan, H. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.160801 · Cited by 112
Quantum field as a quantum cellular automaton: The Dirac free evolution in one dimension
Bisio, A., D’Ariano, G. M., & Tosini, A. (2015) · Annals of Physics · doi:10.1016/j.aop.2014.12.016 · Cited by 81
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Metadata
- Authors
- D'Ariano, G. M., Lo Presti, P., & Paris, M. G. A.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2001-12-12
- Volume
- 87
- Issue
- 27
- Pages
- 270404-270404
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2138966110
- Cited by
- 283
- References
- 4
- FWCI
- 12.02
- Type
- journal article
- Language
- en
- Open access
- green
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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.