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Extremal quantum correlation generation using a hybrid channel.
Rahman, Atta Ur; Ali, Hazrat; Zangi, S M; Qiao, Cong-Feng.
Afiliación
  • Rahman AU; School of Physics, University of Chinese Academy of Science, Yuquan Road 19A, Beijing, 100049, China.
  • Ali H; Abbottabad University of Science and Technology, Havellian, KP, 22500, Pakistan.
  • Zangi SM; School of Physics and Astronomy, Yunnan University, Kunming, 650500, China.
  • Qiao CF; School of Physics, University of Chinese Academy of Science, Yuquan Road 19A, Beijing, 100049, China. qiaocf@ucas.ac.cn.
Sci Rep ; 13(1): 16654, 2023 Oct 03.
Article en En | MEDLINE | ID: mdl-37789025
ABSTRACT
The preservation of quantum correlations requires optimal procedures and the proper design of the transmitting channels. In this regard, we address designing a hybrid channel comprising a single-mode cavity accompanied by a super-Gaussian beam and local dephasing parts based on the dynamics of quantum characteristics. We choose two-level atoms and various functions such as traced-distance discord, concurrence, and local-quantum uncertainty to analyze the effectiveness of the hybrid channel to preserve quantum correlations along with entropy suppression discussed using linear entropy. The joint configuration of the considered fields is found to not only preserve but also generate quantum correlations even in the presence of local dephasing. Most importantly, within certain limits, the proposed channel can be readily regulated to generate maximal quantum correlations and complete suppression of the disorder. Besides, compared to the individual parts, mixing the Fock state cavity, super-Gaussian beam, and local dephasing remains a resourceful choice for the prolonged quantum correlations' preservation. Finally, we present an interrelationship between the considered two-qubit correlations' functions, showing the deviation between each two correlations and of the considered state from maximal entanglement under the influence of the assumed hybrid channel.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: China