Quick Navigation
Topics
Entanglement Theory Quantum Correlations
Doubly minimized Petz and sandwiched Renyi mutual information: Properties
arXiv
Authors: Laura Burri
Year
2024
Paper ID
66973
Status
Preprint
Abstract Read
~2 min
Abstract Words
131
Citations
N/A
Abstract
The doubly minimized Petz Renyi mutual information of order α is defined as the minimization of the Petz divergence of order α of a fixed bipartite quantum state relative to any product state. The doubly minimized sandwiched Renyi mutual information is defined analogously using the sandwiched divergence in place of the Petz divergence. In this work, we establish several properties of these two types of Renyi mutual information. In particular, for the Petz case, we prove additivity for αin [1/2,2]. For the sandwiched case, we establish a novel duality relation for αin \[2/3,infty\] via Sion's minimax theorem, and we subsequently use this duality relation to prove additivity for the same range of α. Previously, additivity for the sandwiched case was known only for αin \[1,infty\], but it had been conjectured to hold for αin \[1/2,infty\].
Why This Paper Matters
- This paper contributes to the Entanglement Theory & Quantum Correlations research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- The doubly minimized Petz Renyi mutual information of order α is defined as the minimization of the Petz divergence of order α of a fixed bipartite quantum state relative to...
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.