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Quantum Information Processing
Erschienen in: Quantum Information Processing 10/2021

01.10.2021

Reversible optical–microwave quantum conversion assisted by optomechanical dynamically dark modes

verfasst von: Ling-Ying Zhu, Yong Dong, Ji Zhang, Cui-Lu Zhai, Yaxin Zhai, Le-Man Kuang

Erschienen in: Quantum Information Processing | Ausgabe 10/2021

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Abstract

We propose a dynamically dark-mode (DDM) scheme to realize the reversible quantum conversion between microwave and optical photons in an electro-optomechanical (EOM) model. It is shown that two DDMs appear at certain times during the dynamical evolution of the EOM model. It is demonstrated that the DDMs can induce two kinds of reversible and highly efficient quantum conversion between the microwave and optical fields, the conditional quantum conversion (CQC) and the entanglement-assisted quantum conversion (EAQC). The CQC happens at the condition of vanishing of the initial-state mean value of one of the microwave and optical fields and only depends on the coupling ratio of the system under consideration. The EAQC occurs in the presence of the initial-state entanglement between the microwave and optical fields. It is found that the EAQC can be manipulated by engineering the initial-state entanglement and the coupling ratio. It is indicated that it is possible to realize the entanglement-enhanced (or suppressed) quantum conversion through controlling the phase of the initial-state parameter. Our work highlights the power of generating reversible and highly efficient quantum conversion between microwave and optical photons by the DDMs.
Vorheriger Artikel Adaptive quantum state tomography with iterative particle filtering
Nächster Artikel Weak measurements, non-classicality and negative probability

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Metadaten
Titel
Reversible optical–microwave quantum conversion assisted by optomechanical dynamically dark modes
verfasst von
Ling-Ying Zhu
Yong Dong
Ji Zhang
Cui-Lu Zhai
Yaxin Zhai
Le-Man Kuang
Publikationsdatum
01.10.2021
Verlag
Springer US
Erschienen in
Quantum Information Processing / Ausgabe 10/2021
Print ISSN: 1570-0755
Elektronische ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-021-03269-9

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