Can entanglement hide behind triangle-free graphs?
- URL: http://arxiv.org/abs/2010.11891v3
- Date: Mon, 29 Mar 2021 15:03:07 GMT
- Title: Can entanglement hide behind triangle-free graphs?
- Authors: Satvik Singh
- Abstract summary: We show that diagonal zero patterns in suitable matrix representations admit a nice description in terms of triangle-free graphs.
We also develop a recipe to construct a plethora of unique classes of positive partial transpose entangled triangle-free states in arbitrary dimensions.
We link the task of entanglement detection in general states to the well-known graph-theoretic problem of finding triangle-free-induced subgraphs in a given graph.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present an alternative approach to unveil a different kind of entanglement
in bipartite quantum states whose diagonal zero patterns in suitable matrix
representations admit a nice description in terms of triangle-free graphs. Upon
application of a local averaging operation, the separability of such states
transforms into a simple matrix positivity condition, the violation of which
implies the presence of entanglement. We completely characterize the class of
triangle-free graphs which allows for nontrivial entanglement detection using
the above test. Moreover, we develop a recipe to construct a plethora of unique
classes of positive partial transpose (PPT) entangled triangle-free states in
arbitrary dimensions. Finally, we link the task of entanglement detection in
general states to the well-known graph-theoretic problem of finding
triangle-free-induced subgraphs in a given graph.
Related papers
- Unifying quantum spatial search, state transfer and uniform sampling on graphs: simple and exact [2.871419116565751]
This article presents a novel and succinct algorithmic framework via alternating quantum walks.
It unifies quantum spatial search, state transfer and uniform sampling on a large class of graphs.
The approach is easy to use since it has a succinct formalism that depends only on the depth of the Laplacian eigenvalue set of the graph.
arXiv Detail & Related papers (2024-07-01T06:09:19Z) - Graph Generation via Spectral Diffusion [51.60814773299899]
We present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process.
Specifically, we propose to use a denoising model to sample eigenvectors and eigenvalues from which we can reconstruct the graph Laplacian and adjacency matrix.
Our permutation invariant model can also handle node features by concatenating them to the eigenvectors of each node.
arXiv Detail & Related papers (2024-02-29T09:26:46Z) - Occupation Number Representation of Graph [2.817211764022392]
We replace the rows of the adjacency matrix of the graph by state vectors in the occupation number representation.
Our method can be used to represent both simple and multigraphs.
arXiv Detail & Related papers (2023-11-21T15:31:48Z) - Spectral Clustering via Orthogonalization-Free Methods [6.460951804337735]
We propose four orthogonalization-free methods for spectral clustering.
In theory, two methods converge to features isomorphic to the eigenvectors corresponding to the smallest eigenvalues of the symmetric normalized Laplacian.
Numerical results are provided to demonstrate the scalability of our methods.
arXiv Detail & Related papers (2023-05-16T16:01:12Z) - Implications of sparsity and high triangle density for graph
representation learning [67.98498239263549]
Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes.
Here, we show that such graphs can be reproduced using an infinite-dimensional inner product model, where the node representations lie on a low-dimensional manifold.
arXiv Detail & Related papers (2022-10-27T09:15:15Z) - Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation [64.49871502193477]
We propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix.
Comprehensive experimental results on six commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods.
arXiv Detail & Related papers (2022-05-21T01:47:17Z) - Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov
Random Fields [51.07460861448716]
This paper presents a convex-analytic framework to learn from data.
We show that a triangular convexity decomposition is guaranteed by a transform of the corresponding to its upper part.
arXiv Detail & Related papers (2021-09-17T17:46:12Z) - 3D Shape Registration Using Spectral Graph Embedding and Probabilistic
Matching [24.41451985857662]
We address the problem of 3D shape registration and we propose a novel technique based on spectral graph theory and probabilistic matching.
The main contribution of this chapter is to extend the spectral graph matching methods to very large graphs by combining spectral graph matching with Laplacian embedding.
arXiv Detail & Related papers (2021-06-21T15:02:31Z) - Spectral clustering under degree heterogeneity: a case for the random
walk Laplacian [83.79286663107845]
This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree.
In the special case of a degree-corrected block model, the embedding concentrates about K distinct points, representing communities.
arXiv Detail & Related papers (2021-05-03T16:36:27Z) - Hamiltonian systems, Toda lattices, Solitons, Lax Pairs on weighted
Z-graded graphs [62.997667081978825]
We identify conditions which allow one to lift one dimensional solutions to solutions on graphs.
We show that even for a simple example of a topologically interesting graph the corresponding non-trivial Lax pairs and associated unitary transformations do not lift to a Lax pair on the Z-graded graph.
arXiv Detail & Related papers (2020-08-11T17:58:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.