Quick Navigation
Topics
Entanglement Theory Quantum Correlations
Zonoids and sparsification of quantum measurements
arXiv
Authors: Guillaume Aubrun, Cécilia Lancien
Year
2013
Paper ID
32559
Status
Preprint
Abstract Read
~2 min
Abstract Words
181
Citations
N/A
Abstract
In this paper, we establish a connection between zonoids (a concept from classical convex geometry) and the distinguishability norms associated to quantum measurements, or POVMs (Positive Operator-Valued Measures), recently introduced in quantum information theory. This correspondence allows us to state and prove the POVM version of classical results from the local theory of Banach spaces about the approximation of zonoids by zonotopes. We show that on mathbf{C}d, the uniform POVM (the most symmetric POVM) can be sparsified, i.e. approximated by a discrete POVM, the latter having only O\(d2\) outcomes. We also show that similar (but weaker) approximation results actually hold for any POVM on mathbf{C}d. By defining an appropriate notion of tensor product for zonoids, we are then able to extend our results to the multipartite setting: we show, roughly speaking, that local POVMs may be sparsified locally. In particular, the local uniform POVM on mathbf{C}d1otimescdotsotimesmathbf{C}dk can be approximated by a discrete POVM which is local and has O\(d12timescdotstimes dk2\) outcomes.
Why This Paper Matters
- This paper contributes to the Entanglement Theory & Quantum Correlations research area in the Quantum Articles archive.
- It adds a 2013 reference point for readers tracking recent quantum research.
- In this paper, we establish a connection between zonoids (a concept from classical convex geometry) and the distinguishability norms associated to quantum measurements, or...
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.