Regression of Concurrence via Local Unitary Invariants
Concurrence is a crucial entanglement measure in quantum theory used to describe the degree of entanglement between two or more qubits. Local unitary (LU) invariants can be employed to describe the relevant properties of quantum states. Compared to quantum state tomography, observing LU invariants c...
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MDPI AG
2024-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/26/11/917 |
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| author | Ming Li Wenjun Wang Xiaoyu Zhang Jing Wang Lei Li Shuqian Shen |
| author_facet | Ming Li Wenjun Wang Xiaoyu Zhang Jing Wang Lei Li Shuqian Shen |
| author_sort | Ming Li |
| collection | DOAJ |
| description | Concurrence is a crucial entanglement measure in quantum theory used to describe the degree of entanglement between two or more qubits. Local unitary (LU) invariants can be employed to describe the relevant properties of quantum states. Compared to quantum state tomography, observing LU invariants can save substantial physical resources and reduce errors associated with tomography. In this paper, we use LU invariants as explanatory variables and employ methods such as multiple regression, tree models, and BP neural network models to fit the concurrence of 2-qubit quantum states. For pure states and Werner states, by analyzing the correlation between data, a functional formula for concurrence in terms of LU invariants is obtained. Additionally, for any two-qubit quantum states, the prediction accuracy for concurrence reaches 98.5%. |
| format | Article |
| id | doaj-art-d6ba05368d2440339bac0ac3c8711f00 |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-d6ba05368d2440339bac0ac3c8711f002025-08-20T02:08:04ZengMDPI AGEntropy1099-43002024-10-01261191710.3390/e26110917Regression of Concurrence via Local Unitary InvariantsMing Li0Wenjun Wang1Xiaoyu Zhang2Jing Wang3Lei Li4Shuqian Shen5College of Science, China University of Petroleum, Qingdao 266580, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaConcurrence is a crucial entanglement measure in quantum theory used to describe the degree of entanglement between two or more qubits. Local unitary (LU) invariants can be employed to describe the relevant properties of quantum states. Compared to quantum state tomography, observing LU invariants can save substantial physical resources and reduce errors associated with tomography. In this paper, we use LU invariants as explanatory variables and employ methods such as multiple regression, tree models, and BP neural network models to fit the concurrence of 2-qubit quantum states. For pure states and Werner states, by analyzing the correlation between data, a functional formula for concurrence in terms of LU invariants is obtained. Additionally, for any two-qubit quantum states, the prediction accuracy for concurrence reaches 98.5%.https://www.mdpi.com/1099-4300/26/11/917concurrenceLocal unitary invariantsregression |
| spellingShingle | Ming Li Wenjun Wang Xiaoyu Zhang Jing Wang Lei Li Shuqian Shen Regression of Concurrence via Local Unitary Invariants Entropy concurrence Local unitary invariants regression |
| title | Regression of Concurrence via Local Unitary Invariants |
| title_full | Regression of Concurrence via Local Unitary Invariants |
| title_fullStr | Regression of Concurrence via Local Unitary Invariants |
| title_full_unstemmed | Regression of Concurrence via Local Unitary Invariants |
| title_short | Regression of Concurrence via Local Unitary Invariants |
| title_sort | regression of concurrence via local unitary invariants |
| topic | concurrence Local unitary invariants regression |
| url | https://www.mdpi.com/1099-4300/26/11/917 |
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