An efficient algebraic representation for graph states for
measurement-based quantum computing
- URL: http://arxiv.org/abs/2212.12102v1
- Date: Fri, 23 Dec 2022 01:40:03 GMT
- Title: An efficient algebraic representation for graph states for
measurement-based quantum computing
- Authors: Sebastiano Corli, Enrico Prati
- Abstract summary: Graph states are main computational building blocks of measurement-based computation.
We show how to efficiently express a graph state through the generators of the stabilizer group.
We provide a framework to manipulate the graph states with a reduced number of stabilizers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph states are the main computational building blocks of measurement-based
computation and a useful tool for error correction in the gate model
architecture. The graph states form a class of quantum states which are
eigenvectors for the abelian group of stabilizer operators. They own
topological properties, arising from their graph structure, including the
presence of highly connected nodes, called hubs. Starting from hub nodes, we
demonstrate how to efficiently express a graph state through the generators of
the stabilizer group. We provide examples by expressing the ring and the star
topology, for which the number of stabilizers reduces from n to n/2, and from n
to 1, respectively. We demonstrate that the graph states can be generated by a
subgroup of the stabilizer group. Therefore, we provide an algebraic framework
to manipulate the graph states with a reduced number of stabilizers.
Related papers
- 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) - Sampling and Uniqueness Sets in Graphon Signal Processing [136.68956350251418]
We study the properties of sampling sets on families of large graphs by leveraging the theory of graphons and graph limits.
We exploit the convergence results to provide an algorithm that obtains approximately close to optimal sampling sets.
arXiv Detail & Related papers (2024-01-11T22:31:48Z) - Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More [30.919536115917726]
We present a probabilistic model based on non-negative matrix factorization which unifies clustering and simplification.
By relaxing the hard clustering to a soft clustering, our algorithm relaxes potentially hard clustering problems to a tractable ones.
arXiv Detail & Related papers (2023-08-12T02:47:57Z) - Graphon Pooling for Reducing Dimensionality of Signals and Convolutional
Operators on Graphs [131.53471236405628]
We present three methods that exploit the induced graphon representation of graphs and graph signals on partitions of [0, 1]2 in the graphon space.
We prove that those low dimensional representations constitute a convergent sequence of graphs and graph signals.
We observe that graphon pooling performs significantly better than other approaches proposed in the literature when dimensionality reduction ratios between layers are large.
arXiv Detail & Related papers (2022-12-15T22:11:34Z) - 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) - 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) - An Entropic Lens on Stabilizer States [0.0]
We show how the two subgraphs already present at two qubits are embedded into more complicated subgraphs at three and four qubits.
We argue that no additional types of subgraph appear beyond four qubits, but that the entropic structures within the subgraphs can grow progressively more complicated as the qubit number increases.
arXiv Detail & Related papers (2022-04-15T18:00:12Z) - Robust Correlation Clustering with Asymmetric Noise [3.8073142980733]
Correlation Clustering is a graph clustering formulation which: (1) takes as input a signed graph with edge weights representing a similarity/dissimilarity measure between the nodes, and (2) requires no prior estimate of the number of clusters in the input graph.
We propose a novel graph generative model, called the Node Factors Model (NFM), which is based on generating feature vectors/embeddings for the graph nodes.
arXiv Detail & Related papers (2021-10-15T21:47:27Z) - GraphStateVis: Interactive Visual Analysis of Qubit Graph States and
their Stabilizer Groups [1.332560004325655]
We introduce GraphStateVis, a web-based application for the visual analysis of qubit graph states and their stabilizer groups.
The user can explore graph-state-specific properties, including the Pauli-weight distribution of its stabilizer operators.
We propose a use case in the context of near-term quantum algorithms to illustrate the capabilities of our prototype.
arXiv Detail & Related papers (2021-05-26T18:00:06Z) - Convergence and Stability of Graph Convolutional Networks on Large
Random Graphs [22.387735135790706]
We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior on standard models of random graphs.
We first study the convergence of GCNs to their continuous counterpart as the number of nodes grows.
We then analyze the stability of GCNs to small deformations of the random graph model.
arXiv Detail & Related papers (2020-06-02T18:36:19Z) - 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)
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.