2019
DOI: 10.1080/03081087.2019.1661347
|View full text |Cite
|
Sign up to set email alerts
|

On decomposable correlation matrices

Abstract: Correlation matrices (positive semidefinite matrices with ones on the diagonal) are of fundamental interest in quantum information theory. In this work we introduce and study the set of r-decomposable correlation matrices: those that can be written as the Schur product of correlation matrices of rank at most r. We find that for all r ≥ 2, every (r + 1) × (r + 1) correlation matrix is r-decomposable, and we construct (2r + 1) × (2r + 1) correlation matrices that are not r-decomposable. One question this leaves … Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

1
6
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
2
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(7 citation statements)
references
References 18 publications
1
6
0
Order By: Relevance
“…We notice that the same result has also been obtained in Ref. [4] by considering the decomposition of a Gram matrix.…”
Section: A the Definition And The Lower Bound Of The Number Of Statessupporting
confidence: 86%
See 3 more Smart Citations
“…We notice that the same result has also been obtained in Ref. [4] by considering the decomposition of a Gram matrix.…”
Section: A the Definition And The Lower Bound Of The Number Of Statessupporting
confidence: 86%
“…Recently a twist on this problem was proposed by some of the authors here independently [4,5], in different mathematical approach but with similar physical meaning. The main idea consists in considering sets of quantum states.…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations
“…It would be interesting to see whether our more general results could be used to characterize preservers of tensor rank r ≥ 2. In [Lov18], the author uses Corollary 9 and Corollary 10 to study the set of decomposable correlation matrices. As one more application, we use Corollary 10 to provide a concise proof of a recent result in [BLM17] (Corollary 11).…”
Section: Introductionmentioning
confidence: 99%