Quick Navigation
Topics
Entanglement Theory Quantum Correlations
Computable lower bound of the parameterized entanglement monotone
arXiv
Authors: Ning Yang, Yu Guo, Shuanping Du
Year
2026
Paper ID
63688
Status
Preprint
Abstract Read
~2 min
Abstract Words
209
Citations
N/A
Abstract
Although numerous measures of entanglement have been proposed so far, the calculation of a given faithful entanglement measure is a hard work since it is always involved in some optimization process. It is, therefore, important to estimate the lower bound of a given entanglement measure for an arbitrary quantum state. This results in a subject of intensive mathematical research. In particular, along this line, the lower bounds of concurrence or other measures that are induced from concurrence have been explored a lot. Here, we investigate the lower bounds of two kinds of entanglement monotones, i.e., q-concurrence (q>1) and α-concurrence (0<α<1), or termed the parameterized entanglement monotone together. We obtain, in the light of the informationally complete (N, M)-positive operator-valued measure [(N, M)-POVM], the lower bounds for the case of frac12<α<1, 1<q<2 for two-qudit states, and the case of 2leqslant q<3 for two-qubit states. We list several examples which show that the lower bounds based on (N, M)-POVM outperform that of GSIC-POVM and SIC-POVM, and all these measurement based bounds are better then the ones induced by positive partial transpose (PPT) and realignment criteria in literature. In addition, we obtain an analytical formula of the parameterized entanglement monotone with frac12<α<1 and 1<q<2 for the isotropic state.
Why This Paper Matters
- This paper contributes to the Entanglement Theory & Quantum Correlations research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- Although numerous measures of entanglement have been proposed so far, the calculation of a given faithful entanglement measure is a hard work since it is always involved in...
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.