Distinguishing Graph States by the Properties of Their Marginals
- URL: http://arxiv.org/abs/2406.09956v2
- Date: Wed, 11 Jun 2025 08:12:01 GMT
- Title: Distinguishing Graph States by the Properties of Their Marginals
- Authors: Lina Vandré, Jarn de Jong, Frederik Hahn, Adam Burchardt, Otfried Gühne, Anna Pappa,
- Abstract summary: We study equivalence relations between graph states under local unitaries (LU)<n>We show that these invariants uniquely identify the entanglement classes of every graph state up to 8 qubits.<n>We generalize tools to test for local Clifford (LC) equivalence of graph states that work by condensing large graphs into smaller graphs.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph states are a class of multi-partite entangled quantum states that are ubiquitous in quantum information. We study equivalence relations between graph states under local unitaries (LU) to obtain distinguishing methods both in local and in networked settings. Based on the marginal structure of graph states, we introduce a family of easy-to-compute LU-invariants. We show that these invariants uniquely identify the entanglement classes of every graph state up to 8 qubits and discuss their reliability for larger numbers of qubits. To handle larger graphs, we generalize tools to test for local Clifford (LC) equivalence of graph states that work by condensing large graphs into smaller graphs. In turn, we show that statements on the equivalence of these smaller graphs (which are easier to compute) can be used to infer statements on the equivalence of the original, larger graphs. We analyze LU-equivalence in two key settings - with and without allowing for the permutation of qubits. We identify entanglement classes, whose marginal structure does not allow us to distinguish them. As a result, we increase the bound on the number of qubits where the LU-LC conjecture holds from 8 to 10 qubits in the setting where qubit permutations are allowed.
Related papers
- Deciding Local Unitary Equivalence of Graph States in Quasi-Polynomial Time [0.0]
We describe an algorithm with quasi-polynomial runtime $nlog_2(n)+O(1)$ for deciding local unitary (LU) equivalence of graph states.<n>We show that LU-equivalence reduces to solving a system of quasi-polynomially many linear equations, avoiding an exponential blow-up.
arXiv Detail & Related papers (2025-02-10T15:34:41Z) - Local equivalence of stabilizer states: a graphical characterisation [0.0]
A fundamental property of graph states is that applying a local complementation results in a graph that represents the same entanglement as the original.
This property served as the cornerstone for capturing non-trivial quantum properties in a simple graphical manner.
We introduce a generalization of local complementation which graphically characterises the LU-equivalence of graph states.
arXiv Detail & Related papers (2024-09-30T10:51:15Z) - MGNet: Learning Correspondences via Multiple Graphs [78.0117352211091]
Learning correspondences aims to find correct correspondences from the initial correspondence set with an uneven correspondence distribution and a low inlier rate.
Recent advances usually use graph neural networks (GNNs) to build a single type of graph or stack local graphs into the global one to complete the task.
We propose MGNet to effectively combine multiple complementary graphs.
arXiv Detail & Related papers (2024-01-10T07:58:44Z) - Fine-grained Graph Rationalization [51.293401030058085]
We propose fine-grained graph rationalization (FIG) for graph machine learning.
Our idea is driven by the self-attention mechanism, which provides rich interactions between input nodes.
Our experiments involve 7 real-world datasets, and the proposed FIG shows significant performance advantages compared to 13 baseline methods.
arXiv Detail & Related papers (2023-12-13T02:56:26Z) - Multipartite Entanglement Distribution in Quantum Networks using Subgraph Complementations [9.32782060570252]
We propose a novel approach for distributing graph states across a quantum network.
We show that the distribution of graph states can be characterized by a system of subgraph complementations.
We find a close to optimal sequence of subgraph complementation operations to distribute an arbitrary graph state.
arXiv Detail & Related papers (2023-08-25T23:03:25Z) - Discrete Graph Auto-Encoder [52.50288418639075]
We introduce a new framework named Discrete Graph Auto-Encoder (DGAE)
We first use a permutation-equivariant auto-encoder to convert graphs into sets of discrete latent node representations.
In the second step, we sort the sets of discrete latent representations and learn their distribution with a specifically designed auto-regressive model.
arXiv Detail & Related papers (2023-06-13T12:40:39Z) - 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) - One-step Bipartite Graph Cut: A Normalized Formulation and Its
Application to Scalable Subspace Clustering [56.81492360414741]
We show how to enforce a one-step normalized cut for bipartite graphs, especially with linear-time complexity.
In this paper, we first characterize a novel one-step bipartite graph cut criterion with normalized constraints, and theoretically prove its equivalence to a trace problem.
We extend this cut criterion to a scalable subspace clustering approach, where adaptive anchor learning, bipartite graph learning, and one-step normalized bipartite graph partitioning are simultaneously modeled.
arXiv Detail & Related papers (2023-05-12T11:27:20Z) - 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) - Efficient tensor network simulation of quantum many-body physics on
sparse graphs [0.0]
We study tensor network states defined on an underlying graph which is sparsely connected.
We find that message-passing inference algorithms can lead to efficient computation of local expectation values.
arXiv Detail & Related papers (2022-06-09T18:00:03Z) - CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph
Similarity Learning [65.1042892570989]
We propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning.
We employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning.
We transform node representations into graph-level representations via pooling operations for graph similarity computation.
arXiv Detail & Related papers (2022-05-30T13:20:26Z) - Partition and Code: learning how to compress graphs [50.29024357495154]
"Partition and Code" framework entails three steps: first, a partitioning algorithm decomposes the graph into elementary structures, then these are mapped to the elements of a small dictionary on which we learn a probability distribution, and finally, an entropy encoder translates the representation into bits.
Our algorithms are quantitatively evaluated on diverse real-world networks obtaining significant performance improvements with respect to different families of non-parametric and parametric graph compressor.
arXiv Detail & Related papers (2021-07-05T11:41:16Z) - From Local Structures to Size Generalization in Graph Neural Networks [53.3202754533658]
Graph neural networks (GNNs) can process graphs of different sizes.
Their ability to generalize across sizes, specifically from small to large graphs, is still not well understood.
arXiv Detail & Related papers (2020-10-17T19:36:54Z) - Verification of graph states in an untrusted network [0.0]
We consider verification of graph states generated by an untrusted source and shared between a network of possibly dishonest parties.
This has implications in certifying the application of graph states for various distributed tasks.
We present a protocol which is globally efficient for a large family of useful graph states.
arXiv Detail & Related papers (2020-07-26T13:17:21Z) - Graph topology inference benchmarks for machine learning [16.857405938139525]
We introduce several benchmarks specifically designed to reveal the relative merits and limitations of graph inference methods.
We also contrast some of the most prominent techniques in the literature.
arXiv Detail & Related papers (2020-07-16T09:40:32Z) - Inverse Graph Identification: Can We Identify Node Labels Given Graph
Labels? [89.13567439679709]
Graph Identification (GI) has long been researched in graph learning and is essential in certain applications.
This paper defines a novel problem dubbed Inverse Graph Identification (IGI)
We propose a simple yet effective method that makes the node-level message passing process using Graph Attention Network (GAT) under the protocol of GI.
arXiv Detail & Related papers (2020-07-12T12:06:17Z) - Graph Pooling with Node Proximity for Hierarchical Representation
Learning [80.62181998314547]
We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology.
Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
arXiv Detail & Related papers (2020-06-19T13:09:44Z) - Block-Approximated Exponential Random Graphs [77.4792558024487]
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs.
We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions.
Our methods are scalable to sparse graphs consisting of millions of nodes.
arXiv Detail & Related papers (2020-02-14T11:42:16Z) - The Power of Graph Convolutional Networks to Distinguish Random Graph
Models: Short Version [27.544219236164764]
Graph convolutional networks (GCNs) are a widely used method for graph representation learning.
We investigate the power of GCNs to distinguish between different random graph models on the basis of the embeddings of their sample graphs.
arXiv Detail & Related papers (2020-02-13T17:58:42Z)
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.