Quick Navigation
Topics
Open Quantum Systems Decoherence
Entanglement Theory Quantum Correlations
Quantum Simulation
Breaking the curse of dimension in multi-marginal Kantorovich optimal transport on finite state spaces
arXiv
Authors: G. Friesecke, D. Vögler
Year
2017
Paper ID
39197
Status
Preprint
Abstract Read
~2 min
Abstract Words
150
Citations
N/A
Abstract
We present a new ansatz space for the general symmetric multi-marginal Kantorovich optimal transport problem on finite state spaces which reduces the number of unknowns from tbinom{N+ell-1}{ell-1} to ellcdot(N+1), where ell is the number of marginal states and N the number of marginals. The new ansatz space is a careful low-dimensional enlargement of the Monge class, which corresponds to ellcdot(N-1) unknowns, and cures the insufficiency of the Monge ansatz, i.e. we show that the Kantorovich problem always admits a minimizer in the enlarged class, for arbitrary cost functions. Our results apply, in particular, to the discretization of multi-marginal optimal transport with Coulomb cost in three dimensions, which has received much recent interest due to its emergence as the strongly correlated limit of Hohenberg-Kohn density functional theory. In this context N corresponds to the number of particles, motivating the interest in large N.
Why This Paper Matters
- This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
- It adds a 2017 reference point for readers tracking recent quantum research.
- We present a new ansatz space for the general symmetric multi-marginal Kantorovich optimal transport problem on finite state spaces which reduces the number of unknowns from...
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.