Bell pair extraction using graph foliage techniques
- URL: http://arxiv.org/abs/2311.16188v2
- Date: Wed, 21 Feb 2024 04:03:35 GMT
- Title: Bell pair extraction using graph foliage techniques
- Authors: Derek Zhang
- Abstract summary: We are interested in whether multiple pairs can communicate simultaneously across a network.
Quantum networks can be represented with graph states, and producing communication links amounts to performing certain quantum operations on graph states.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Future quantum networks can facilitate communication of quantum information
between various nodes. We are particularly interested in whether multiple pairs
can communicate simultaneously across a network. Quantum networks can be
represented with graph states, and producing communication links amounts to
performing certain quantum operations on graph states. This problem can be
formulated in a graph-theoretic sense with the (Bell) vertex-minor problem. We
discuss the recently introduced foliage partition and provide a generalization.
This generalization leads us to a useful result for approaching the
vertex-minor problem. We apply this result to identify the exact solution for
the Bell vertex-minor problem on line, tree, and ring graphs.
Related papers
- InstructG2I: Synthesizing Images from Multimodal Attributed Graphs [50.852150521561676]
We propose a graph context-conditioned diffusion model called InstructG2I.
InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling.
A Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process.
arXiv Detail & Related papers (2024-10-09T17:56:15Z) - 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) - NodeFormer: A Scalable Graph Structure Learning Transformer for Node
Classification [70.51126383984555]
We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes.
The efficient computation is enabled by a kernerlized Gumbel-Softmax operator.
Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
arXiv Detail & Related papers (2023-06-14T09:21:15Z) - Graph Mixup with Soft Alignments [49.61520432554505]
We study graph data augmentation by mixup, which has been used successfully on images.
We propose S-Mixup, a simple yet effective mixup method for graph classification by soft alignments.
arXiv Detail & Related papers (2023-06-11T22:04:28Z) - From Quantum Graph Computing to Quantum Graph Learning: A Survey [86.8206129053725]
We first elaborate the correlations between quantum mechanics and graph theory to show that quantum computers are able to generate useful solutions.
For its practicability and wide-applicability, we give a brief review of typical graph learning techniques.
We give a snapshot of quantum graph learning where expectations serve as a catalyst for subsequent research.
arXiv Detail & Related papers (2022-02-19T02:56:47Z) - Graphon-aided Joint Estimation of Multiple Graphs [24.077455621015552]
We consider the problem of estimating the topology of multiple networks from nodal observations.
We adopt a graphon as our random graph model, which is a nonparametric model from which graphs of potentially different sizes can be drawn.
arXiv Detail & Related papers (2022-02-11T15:20:44Z) - Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised
Node Classification [59.06717774425588]
We propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.
It contains explicit pairwise factors to model output-output relations and uses a GNN backbone to model input-output relations.
We conduct experiments on various datasets, which shows that our model can effectively improve the performance for semi-supervised node classification on graphs.
arXiv Detail & Related papers (2021-07-27T19:47:53Z) - Graph Partitioning into Hamiltonian Subgraphs on a Quantum Annealer [0.0]
We show that a quantum annealer can be used to solve the NP-complete problem of graph partitioning into subgraphs.
We formulate the problem as a quadratic unconstrained binary optimisation and run it on a D-Wave Advantage quantum annealer.
arXiv Detail & Related papers (2021-04-18T16:15:00Z) - Factorizable Graph Convolutional Networks [90.59836684458905]
We introduce a novel graph convolutional network (GCN) that explicitly disentangles intertwined relations encoded in a graph.
FactorGCN takes a simple graph as input, and disentangles it into several factorized graphs.
We evaluate the proposed FactorGCN both qualitatively and quantitatively on the synthetic and real-world datasets.
arXiv Detail & Related papers (2020-10-12T03:01:40Z) - Distributing Graph States Across Quantum Networks [16.74626042261441]
We consider a quantum network consisting of nodes-quantum computers within which local operations are free-and EPR pairs shared between nodes that can continually be generated.
We prove upper bounds for our approach on the number of EPR pairs consumed, number of timesteps taken, and amount of classical communication required.
arXiv Detail & Related papers (2020-09-23T01:36:12Z) - Generation and Robustness of Quantum Entanglement in Spin Graphs [0.0]
Entanglement is a crucial resource for quantum information processing.
We show how a graph structure can be used to generate high fidelity entangled states.
We also investigate how fabrication errors affect the entanglement generation protocol.
arXiv Detail & Related papers (2020-02-18T16:11:57Z)
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.