Cost vector analysis & multi-path entanglement routing in quantum networks

• URL: http://arxiv.org/abs/2105.00418v3
• Date: Thu, 8 Aug 2024 22:48:42 GMT
• Title: Cost vector analysis & multi-path entanglement routing in quantum networks
• Authors: Hudson Leone, Nathaniel R. Miller, Deepesh Singh, Nathan K. Langford, Peter P. Rohde,
• Abstract summary: We present a framework for analysing quantum routing protocols that we call the textitcost-vector formalism. We develop rudimentary greedy algorithms for routing in this framework and test them over a variety of toy networking scenarios. Our results indicate that congestion in quantum networks does not improve significantly when more nodes (computers) are added. Rather, we find that congestion is all but eliminated by waiting a small amount of time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a static framework for analysing quantum routing protocols that we call the \textit{cost-vector formalism}. Here, quantum networks are recast as multi-graphs where edges represent two-qubit entanglement resources that \textit{could} exist under some sequence of operations. Each edge is weighted with a \textit{transmission probability} that represents the likelihood of the pair existing and a \textit{coherence probability} which is the likelihood that the pair is suitable for teleportation. Routing operations such as entanglement swapping and purification are then interpreted as \textit{contractions on the multi-graph} with relatively simple rules for updating the edge-weights. Moreover, we extend our formalism to include routing scenarios over time by developing a compatible resource theory for quantum memories. We develop rudimentary greedy algorithms for routing in this framework and test them over a variety of toy networking scenarios. Our results indicate that congestion in quantum networks does not improve significantly when more nodes (computers) are added. Rather, we find that congestion is all but eliminated by waiting a small amount of time.

Related papers

• Multipartite Entanglement Routing as a Hypergraph Immersion Problem [4.3301675903966625]
  Multipartite entanglement, linking multiple nodes simultaneously, is a higher-order correlation that offers advantages over pairwise connections in quantum networks (QNs) Here, we address the question of whether a QN can be topologically transformed into another via entanglement routing. Our key result is an exact mapping from multipartite entanglement routing to Nash-Williams's graph immersion problem, extended to hypergraphs.
  arXiv  Detail & Related papers  (2024-06-19T11:21:00Z)
• Distributing circuits over heterogeneous, modular quantum computing network architectures [2.550561768977603]
  We consider a heterogeneous network of quantum computing modules, sparsely connected via Bell states. Operations across these connections constitute a computational bottleneck. We introduce several techniques for transforming a given quantum circuit into one implementable on a network of the aforementioned type.
  arXiv  Detail & Related papers  (2023-05-23T15:14:53Z)
• Multi-User Entanglement Distribution in Quantum Networks Using Multipath Routing [55.2480439325792]
  We propose three protocols that increase the entanglement rate of multi-user applications by leveraging multipath routing. The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation.
  arXiv  Detail & Related papers  (2023-03-06T18:06:00Z)
• Quantum routing in planar graph using perfect state transfer [0.0]
  In this article, we consider a spin-spin interaction network governed by $XX + YY$ Hamiltonian. We take a privilege to switch on or off any interaction, that assists us to perform multiple perfect state transfers in a graph simultaneously.
  arXiv  Detail & Related papers  (2023-02-20T16:30:23Z)
• Scaling Network Topologies for Multi-User Entanglement Distribution [0.0]
  Future quantum internet relies on large-scale entanglement distribution. Quantum decoherence is a significant obstacle in large-scale networks, which otherwise perform better with multiple paths between the source and destination. We propose a new topology, connected tree, with a significant amount of redundant edges to support multi-path routing of entangled pairs.
  arXiv  Detail & Related papers  (2022-12-06T10:48:08Z)
• Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss [61.26793005355441]
  Cluster-Promoting Quantization (CPQ) finds the optimal quantization grids for neural networks. DropBits is a new bit-drop technique that revises the standard dropout regularization to randomly drop bits instead of neurons. We experimentally validate our method on various benchmark datasets and network architectures.
  arXiv  Detail & Related papers  (2021-09-05T15:15:07Z)
• Artificial Neural Networks generated by Low Discrepancy Sequences [59.51653996175648]
  We generate artificial neural networks as random walks on a dense network graph. Such networks can be trained sparse from scratch, avoiding the expensive procedure of training a dense network and compressing it afterwards. We demonstrate that the artificial neural networks generated by low discrepancy sequences can achieve an accuracy within reach of their dense counterparts at a much lower computational complexity.
  arXiv  Detail & Related papers  (2021-03-05T08:45:43Z)
• Purification and Entanglement Routing on Quantum Networks [55.41644538483948]
  A quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users. We introduce effectives enabling fast path-finding algorithms for maximizing entanglement shared between two nodes on a quantum network.
  arXiv  Detail & Related papers  (2020-11-23T19:00:01Z)
• Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks [78.65792427542672]
  Dynamic Graph Network (DG-Net) is a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent connection paths. Instead of using the same path of the network, DG-Net aggregates features dynamically in each node, which allows the network to have more representation ability.
  arXiv  Detail & Related papers  (2020-10-02T16:50:26Z)
• Learning Dynamic Routing for Semantic Segmentation [86.56049245100084]
  This paper studies a conceptually new method to alleviate the scale variance in semantic representation, named dynamic routing. The proposed framework generates data-dependent routes, adapting to the scale distribution of each image. To this end, a differentiable gating function, called soft conditional gate, is proposed to select scale transform paths on the fly.
  arXiv  Detail & Related papers  (2020-03-23T17:22:14Z)

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.