Quantifying quantum entanglement via machine learning models

Changchun Feng, Lin Chen

Communications in Theoretical Physics ›› 2024, Vol. 76 ›› Issue (7) : 75104.

PDF(324 KB)
Welcome to visit Communications in Theoretical Physics,
PDF(324 KB)
Communications in Theoretical Physics ›› 2024, Vol. 76 ›› Issue (7) : 75104. DOI: 10.1088/1572-9494/ad4090  CSTR: 32041.14.CTP.1572-9494/ad4090
Quantum Physics and Quantum Information

Quantifying quantum entanglement via machine learning models

    {{javascript:window.custom_author_en_index=0;}}
  • {{article.zuoZhe_EN}}
Author information +
History +

HeighLight

{{article.keyPoints_en}}

Abstract

{{article.zhaiyao_en}}

Key words

QR code of this article

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
{{article.zuoZheEn_L}}. {{article.title_en}}[J]. {{journal.qiKanMingCheng_EN}}, 2024, 76(7): 75104 https://doi.org/10.1088/1572-9494/ad4090

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

References

{{article.reference}}

Funding

RIGHTS & PERMISSIONS

{{article.copyrightStatement_en}}
{{article.copyrightLicense_en}}
PDF(324 KB)

Accesses

Citation

Detail
Sections
Recommended
Copyright © Editorial Office of Communications in Theoretical Physics
Address: Institute of Theoretical Physics, Chinese Academy of Sciences,
Beijing 100190, China　
E-mail：ctp@itp.ac.cn
Website: https://ctp.itp.ac.cn　https://iopscience.org/ctp
Powered by Beijing Magtech Co., Ltd.

/

〈 〉