Cost and Routing of Continuous Variable Quantum Networks
- URL: http://arxiv.org/abs/2108.08176v3
- Date: Wed, 21 Jun 2023 10:15:04 GMT
- Title: Cost and Routing of Continuous Variable Quantum Networks
- Authors: Federico Centrone, Frederic Grosshans and Valentina Parigi
- Abstract summary: We report for their cost as a global measure of squeezing and number of squeezed modes that are necessary to build the network.
We show that homodyne measurements along parallel paths between two nodes allow to increase the final entanglement in these nodes and we use this effect to boost the efficiency of an entanglement routing protocol.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study continuous-variable graph states with regular and complex network
shapes and we report for their cost as a global measure of squeezing and number
of squeezed modes that are necessary to build the network. We provide an
analytical formula to compute the experimental resources required to implement
the graph states and we use it to show that the scaling of the squeezing cost
with the size of the network strictly depends on its topology. We show that
homodyne measurements along parallel paths between two nodes allow to increase
the final entanglement in these nodes and we use this effect to boost the
efficiency of an entanglement routing protocol. The devised routing protocol is
particularly efficient in running-time for complex sparse networks.
Related papers
- Multi-tree Quantum Routing in Realistic Topologies [0.19972837513980318]
We present a multi-tree approach with multiple DODAGs designed to improve end-to-end entanglement rates in large-scale networks.
Our simulations show a marked improvement in end-to-end entanglement rates for specific topologies compared to the single-tree method.
arXiv Detail & Related papers (2024-08-12T14:58:02Z) - Network Alignment with Transferable Graph Autoencoders [79.89704126746204]
We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings.
We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs.
Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
arXiv Detail & Related papers (2023-10-05T02:58:29Z) - Performance metrics for the continuous distribution of entanglement in
multi-user quantum networks [0.0]
Entangled states shared among distant nodes are frequently used in quantum network applications.
In this paper, we focus on the steady-state performance analysis of protocols for continuous distribution of entanglement.
One of the main conclusions from our analysis is that the entanglement consumption rate has a greater impact on the protocol performance than the fidelity requirements.
arXiv Detail & Related papers (2023-07-03T23:55:02Z) - Entanglement Routing and Bottlenecks in Grid Networks [0.0]
Existing protocols like $X$ protocol use graph theoretic tools like local complementation to optimize the number of measurements required to extract any Bell pair among the network users.
Here, the existing results are extended to establish a counter-intuitive notion that, in general, the most optimal path to perform the $X$ protocol is not along the shortest path.
Bottlenecks in establishing simultaneous Bell pairs in nearest-neighbor architectures are also explored.
arXiv Detail & Related papers (2022-11-22T19:09:01Z) - Multiparty Entanglement Routing in Quantum Networks [0.0]
A protocol is proposed for extracting maximally entangled (GHZn) states for any number of parties in quantum networks.
The protocol only requires local measurements at the network nodes and just a single qubit memory per user.
arXiv Detail & Related papers (2022-11-12T15:40:34Z) - 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) - Fitting the Search Space of Weight-sharing NAS with Graph Convolutional
Networks [100.14670789581811]
We train a graph convolutional network to fit the performance of sampled sub-networks.
With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
arXiv Detail & Related papers (2020-04-17T19:12:39Z) - Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs.
Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2020-04-06T15:51:00Z) - Progressive Graph Convolutional Networks for Semi-Supervised Node
Classification [97.14064057840089]
Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification.
We propose a method to automatically build compact and task-specific graph convolutional networks.
arXiv Detail & Related papers (2020-03-27T08:32:16Z) - Toward fast and accurate human pose estimation via soft-gated skip
connections [97.06882200076096]
This paper is on highly accurate and highly efficient human pose estimation.
We re-analyze this design choice in the context of improving both the accuracy and the efficiency over the state-of-the-art.
Our model achieves state-of-the-art results on the MPII and LSP datasets.
arXiv Detail & Related papers (2020-02-25T18:51:51Z)
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.