Real state transfer on edge perturbed graphs with generalized clusters
- URL: http://arxiv.org/abs/2505.07982v2
- Date: Fri, 20 Jun 2025 06:25:20 GMT
- Title: Real state transfer on edge perturbed graphs with generalized clusters
- Authors: Hiranmoy Pal,
- Abstract summary: We study the existence of real state transfer in edge-perturbed graphs containing generalized clusters.<n>A central observation is that the evolution of certain quantum states depends solely on the local structure of the underlying graph.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the existence of real state transfer in edge-perturbed graphs containing generalized clusters, where the Hamiltonian is taken to be either the adjacency matrix, the Laplacian matrix, or the signless Laplacian matrix of an associated weighted graph. This framework provides a unified approach for constructing new graphs that exhibit perfect real state transfer, building on known examples with this property. A central observation is that the evolution of certain quantum states depends solely on the local structure of the underlying graph. In particular, we construct an infinite family of graphs with maximum valency five that exhibit perfect pair state transfer-under each of the aforementioned matrices-between the same pair of states at the same time, despite being non-regular. Additionally, we identify instances of perfect pair state transfer in edge-perturbed graphs, including complete graphs, complete bipartite graphs, blow-up graphs, and related structures. We also examine various graph operations-such as the sequential join, complement, Cartesian product, lexicographic product, and corona product-that generate new families of graphs exhibiting perfect real state transfer with respect to all three choices of the Hamiltonian.
Related papers
- Strongly regular and strongly walk-regular graphs that admit perfect state transfer [0.0]
We study perfect state transfer in Grover walks on two important classes of graphs: strongly regular graphs and strongly walk-regular graphs.<n>We first give a complete classification of strongly regular graphs that admit perfect state transfer. The only such graphs are the complete bipartite graph $K_2,2 and the complete graph $K_2,2,2.
arXiv Detail & Related papers (2025-06-03T07:10:06Z) - A generalization of quantum pair state transfer [0.0]
An $s$-pair state in a graph is a quantum state of the form $mathbfe_u+smathbfe_v$.
We develop the theory of perfect $s$-pair state transfer in continuous quantum walks.
arXiv Detail & Related papers (2024-04-25T14:45:49Z) - GraphRCG: Self-Conditioned Graph Generation [78.69810678803248]
We propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions.
Our framework demonstrates superior performance over existing state-of-the-art graph generation methods in terms of graph quality and fidelity to training data.
arXiv Detail & Related papers (2024-03-02T02:28:20Z) - Curve Your Attention: Mixed-Curvature Transformers for Graph
Representation Learning [77.1421343649344]
We propose a generalization of Transformers towards operating entirely on the product of constant curvature spaces.
We also provide a kernelized approach to non-Euclidean attention, which enables our model to run in time and memory cost linear to the number of nodes and edges.
arXiv Detail & Related papers (2023-09-08T02:44:37Z) - The Foliage Partition: An Easy-to-Compute LC-Invariant for Graph States [0.0]
This paper introduces the foliage partition, an easy-to-compute LC-invariant for graph states.<n>Inspired by the foliage of a graph, our invariant has a natural graphical representation in terms of leaves, axils, and twins.
arXiv Detail & Related papers (2023-05-12T17:55:45Z) - OrthoReg: Improving Graph-regularized MLPs via Orthogonality
Regularization [66.30021126251725]
Graph Neural Networks (GNNs) are currently dominating in modeling graphstructure data.
Graph-regularized networks (GR-MLPs) implicitly inject the graph structure information into model weights, while their performance can hardly match that of GNNs in most tasks.
We show that GR-MLPs suffer from dimensional collapse, a phenomenon in which the largest a few eigenvalues dominate the embedding space.
We propose OrthoReg, a novel GR-MLP model to mitigate the dimensional collapse issue.
arXiv Detail & Related papers (2023-01-31T21:20:48Z) - GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton.
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
arXiv Detail & Related papers (2022-12-01T11:49:07Z) - Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution [60.695162101159134]
Existing works merely view a dynamic graph as a sequence of changes.
We formulate dynamic graphs as temporal edge sequences associated with joining time of.
vertex and timespan of edges.
A time-aware Transformer is proposed to embed.
vertex' dynamic connections and ToEs into the learned.
vertex representations.
arXiv Detail & Related papers (2022-07-01T15:32:56Z) - Graph Spectral Embedding using the Geodesic Betweeness Centrality [76.27138343125985]
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure.
GSE uses the solution of the Sylvester equation to capture both network structure and neighborhood proximity in a single representation.
arXiv Detail & Related papers (2022-05-07T04:11:23Z) - Quantum state transfer between twins in weighted graphs [0.0]
We explore the role of twin vertices in quantum state transfer.
We provide characterizations of periodicity, perfect state transfer, and pretty good state transfer.
As an application, we provide characterizations of all simple unweighted double cones on regular graphs that exhibit periodicity, perfect state transfer, and pretty good state transfer.
arXiv Detail & Related papers (2022-01-08T01:15:24Z) - Strong Cospectrality and Twin Vertices in Weighted Graphs [0.0]
We show that a pair of twin vertices in a weighted graph exhibits strong cospectrality with respect to arbitrary Hermitian matrices.
We also generalize known results about equitable and almost equitable partitions, and use these to determine which joins of the form $Xvee H$, where $X$ is either the complete or empty graph, exhibit strong cospectrality.
arXiv Detail & Related papers (2021-11-01T21:18:42Z) - Boundary theories of critical matchgate tensor networks [59.433172590351234]
Key aspects of the AdS/CFT correspondence can be captured in terms of tensor network models on hyperbolic lattices.
For tensors fulfilling the matchgate constraint, these have previously been shown to produce disordered boundary states.
We show that these Hamiltonians exhibit multi-scale quasiperiodic symmetries captured by an analytical toy model.
arXiv Detail & Related papers (2021-10-06T18:00:03Z) - Pretty good state transfer in discrete-time quantum walks [0.0]
We establish the theory for pretty good state transfer in discrete-time quantum walks.
For a class of walks, we show that pretty good state transfer is characterized by the spectrum of certain Hermitian adjacency matrix of the graph.
arXiv Detail & Related papers (2021-05-08T18:55:57Z) - 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) - Wasserstein-based Graph Alignment [56.84964475441094]
We cast a new formulation for the one-to-many graph alignment problem, which aims at matching a node in the smaller graph with one or more nodes in the larger graph.
We show that our method leads to significant improvements with respect to the state-of-the-art algorithms for each of these tasks.
arXiv Detail & Related papers (2020-03-12T22:31:59Z) - Perfect State Transfer on Oriented Graphs [0.0]
We study the phenomena, unique to oriented graphs, of multiple state transfer.
We give a characterization of multiple state transfer, and a new example of a graph where it occurs.
arXiv Detail & Related papers (2020-02-11T20:34:54Z) - Bridging Knowledge Graphs to Generate Scene Graphs [49.69377653925448]
We propose a novel graph-based neural network that iteratively propagates information between the two graphs, as well as within each of them.
Our Graph Bridging Network, GB-Net, successively infers edges and nodes, allowing to simultaneously exploit and refine the rich, heterogeneous structure of the interconnected scene and commonsense graphs.
arXiv Detail & Related papers (2020-01-07T23:35:52Z)
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.