Network Centralities in Quantum Entanglement Distribution due to User Preferences

• URL: http://arxiv.org/abs/2308.08170v1
• Date: Wed, 16 Aug 2023 07:00:09 GMT
• Title: Network Centralities in Quantum Entanglement Distribution due to User Preferences
• Authors: Dibakar Das, Shiva Kumar Malapaka, Jyotsna Bapat, Debabrata Das
• Abstract summary: This paper studies the centralities of the network when the link layer topology of entanglements is driven by usage patterns of peer-to-peer connections. It shows that the edge centralities (measured as usage of individual edges during entanglement distribution) of the entangled graph follow power law distributions. These findings will help in quantum resource management, e.g., quantum technology with high reliability and lower decoherence time may be allocated to edges with high centralities.
• Score: 5.243460995467895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum networks are of great interest of late which apply quantum mechanics to transfer information securely. One of the key properties which are exploited is entanglement to transfer information from one network node to another. Applications like quantum teleportation rely on the entanglement between the concerned nodes. Thus, efficient entanglement distribution among network nodes is of utmost importance. Several entanglement distribution methods have been proposed in the literature which primarily rely on attributes, such as, fidelities, link layer network topologies, proactive distribution, etc. This paper studies the centralities of the network when the link layer topology of entanglements (referred to as entangled graph) is driven by usage patterns of peer-to-peer connections between remote nodes (referred to as connection graph) with different characteristics. Three different distributions (uniform, gaussian, and power law) are considered for the connection graph where the two nodes are selected from the same distribution. For the entangled graph, both reactive and proactive entanglements are employed to form a random graph. Results show that the edge centralities (measured as usage frequencies of individual edges during entanglement distribution) of the entangled graph follow power law distributions whereas the growth in entanglements with connections and node centralities (degrees of nodes) are monomolecularly distributed for most of the scenarios. These findings will help in quantum resource management, e.g., quantum technology with high reliability and lower decoherence time may be allocated to edges with high centralities.

Related papers

• A Quantum Algorithm for Assessing Node Importance in the st-Connectivity Attack [0.8739101659113155]
  Problems in distributed security often map naturally to graphs. Cooperative game theory has been used to create nuanced and flexible notions of node centrality. This work describes a quantum approach to approximating the importance of nodes that maintain a target connection.
  arXiv  Detail & Related papers  (2025-02-01T14:40:52Z)
• Space-time Peer-to-Peer Distribution of Multi-party Entanglement for Any Quantum Network [1.8534793212973737]
  We propose a novel quantum network protocol to efficiently implement the general graph state distribution in the network layer. An explicit mathematical model for a general graph state distribution problem has been constructed. We leverage the spacetime quantum network inspired by the symmetry from relativity for memory management in network problems.
  arXiv  Detail & Related papers  (2024-12-19T11:37:32Z)
• Multi-perspective Memory Enhanced Network for Identifying Key Nodes in Social Networks [51.54002032659713]
  We propose a novel Multi-perspective Memory Enhanced Network (MMEN) for identifying key nodes in social networks. MMEN mines key nodes from multiple perspectives and utilizes memory networks to store historical information. Our method significantly outperforms previous methods.
  arXiv  Detail & Related papers  (2024-03-22T14:29:03Z)
• GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection [51.608147732998994]
  Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
  arXiv  Detail & Related papers  (2024-01-17T13:04:23Z)
• Entanglement Routing Based on Fidelity Curves [0.0]
  We consider quantum networks where each link is characterized by a trade-off between the entanglement generation rate and fidelity. Two entanglement distribution models are considered: one where entangled qubits are distributed one at a time, and a flow model where a large number of entangled qubits are distributed simultaneously.
  arXiv  Detail & Related papers  (2023-03-22T18:59:36Z)
• p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks [15.164084925877624]
  We aim to classify nodes of unstructured peer-to-peer networks with communication uncertainty, such as users of decentralized social networks. We employ decoupled Graph Neural Networks (GNNs) to solve this problem. We develop an asynchronous decentralized formulation of diffusion that converges at the same predictions linearly with respect to communication rate.
  arXiv  Detail & Related papers  (2021-11-29T12:21:47Z)
• Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical [85.0376670244522]
  We investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. We develop a Star-shaped Reasoning Graph Network (S-RGN) to exploit more powerful and flexible capacity. We also develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity.
  arXiv  Detail & Related papers  (2021-09-06T03:16:56Z)
• A Proactive Connection Setup Mechanism for Large Quantum Networks [0.0]
  It is necessary to have an efficient mechanism to distribute entanglement among quantum network nodes. This paper presents a novel way to quicken connection setup between two nodes using historical data. Results show, with quantum network size increase, the proposed approach improves success rate of connection establishments.
  arXiv  Detail & Related papers  (2020-12-25T11:48:40Z)
• Spectral Embedding of Graph Networks [76.27138343125985]
  We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure. The embedding is based on a generalized graph Laplacian, whose eigenvectors compactly capture both network structure and neighborhood proximity in a single representation.
  arXiv  Detail & Related papers  (2020-09-30T04:59:10Z)
• Critical Phenomena in Complex Networks: from Scale-free to Random Networks [77.34726150561087]
  We study critical phenomena in a class of configuration network models with hidden variables controlling links between pairs of nodes. We find analytical expressions for the average node degree, the expected number of edges, and the Landau and Helmholtz free energies, as a function of the temperature and number of nodes.
  arXiv  Detail & Related papers  (2020-08-05T18:57:38Z)
• Quantized Decentralized Stochastic Learning over Directed Graphs [54.005946490293496]
  We consider a decentralized learning problem where data points are distributed among computing nodes communicating over a directed graph. As the model size gets large, decentralized learning faces a major bottleneck that is the communication load due to each node transmitting messages (model updates) to its neighbors. We propose the quantized decentralized learning algorithm over directed graphs that is based on the push-sum algorithm in decentralized consensus optimization.
  arXiv  Detail & Related papers  (2020-02-23T18:25:39Z)

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.