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Mapping photonic entanglement into and out of a quantum memory.

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Title: Mapping photonic entanglement into and out of a quantum memory.
Authors: Choi, K. S., Deng, H., Laurat, J., Kimble, H. J.
Source: Nature. 3/6/2008, Vol. 452 Issue 7183, p67-71. 5p. 1 Diagram, 1 Chart, 2 Graphs.
Subjects: Analytic mappings, Quantum theory, Photonics, Photons, Information science, Information processing, Memory maps (Computer science), Laser cooling, Nuclear physics
Abstract: Developments in quantum information science rely critically on entanglement—a fundamental aspect of quantum mechanics that causes parts of a composite system to show correlations stronger than can be explained classically. In particular, scalable quantum networks require the capability to create, store and distribute entanglement among distant matter nodes by means of photonic channels. Atomic ensembles can play the role of such nodes. So far, in the photon-counting regime, heralded entanglement between atomic ensembles has been successfully demonstrated through probabilistic protocols. But an inherent drawback of this approach is the compromise between the amount of entanglement and its preparation probability, leading to intrinsically low count rates for high entanglement. Here we report a protocol where entanglement between two atomic ensembles is created by coherent mapping of an entangled state of light. By splitting a single photon and performing subsequent state transfer, we separate the generation of entanglement and its storage. After a programmable delay, the stored entanglement is mapped back into photonic modes with overall efficiency of 17%. Together with improvements in single-photon sources, our protocol will allow ‘on-demand’ entanglement of atomic ensembles, a powerful resource for quantum information science. [ABSTRACT FROM AUTHOR]
Copyright of Nature is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Developments in quantum information science rely critically on entanglement—a fundamental aspect of quantum mechanics that causes parts of a composite system to show correlations stronger than can be explained classically. In particular, scalable quantum networks require the capability to create, store and distribute entanglement among distant matter nodes by means of photonic channels. Atomic ensembles can play the role of such nodes. So far, in the photon-counting regime, heralded entanglement between atomic ensembles has been successfully demonstrated through probabilistic protocols. But an inherent drawback of this approach is the compromise between the amount of entanglement and its preparation probability, leading to intrinsically low count rates for high entanglement. Here we report a protocol where entanglement between two atomic ensembles is created by coherent mapping of an entangled state of light. By splitting a single photon and performing subsequent state transfer, we separate the generation of entanglement and its storage. After a programmable delay, the stored entanglement is mapped back into photonic modes with overall efficiency of 17%. Together with improvements in single-photon sources, our protocol will allow ‘on-demand’ entanglement of atomic ensembles, a powerful resource for quantum information science. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Nature is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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