Entanglement Routing Based on Fidelity Curves

• URL: http://arxiv.org/abs/2303.12864v3
• Date: Mon, 9 Sep 2024 15:15:22 GMT
• Title: Entanglement Routing Based on Fidelity Curves
• Authors: Bruno C. Coutinho, Raul Monteiro, Luís Bugalho, Francisco A. Monteiro,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to efficiently distribute entanglement over large-scale quantum networks is still an open problem that greatly depends on the technology considered. In this work, we consider quantum networks where each link is characterized by a trade-off between the entanglement generation rate and fidelity. For such networks, we look at the two following problems: the one of finding the best path to connect any two given nodes, and the problem of finding the best starting node in order to connect three nodes in the network multipartite entanglement routing. 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. The paper proposes of a quite general methodology that uses continuous fidelity curves (i.e., entanglement generation fidelity vs. rate) as the main routing metric. Combined with multi-objective path-finding algorithms, the fidelity curves describing each link allow finding a set of paths that maximize both the end-to-end fidelity and the entanglement generation rate. For the link models and networks considered, it is proven that the algorithm always converges to the optimal solution. It is also shown through simulation that the execution time grows polynomially with the number of network nodes (growing with a power between $1$ and $1.4$, depending on the network)

Related papers

• Hierarchical Multi-Marginal Optimal Transport for Network Alignment [52.206006379563306]
  Multi-network alignment is an essential prerequisite for joint learning on multiple networks. We propose a hierarchical multi-marginal optimal transport framework named HOT for multi-network alignment. Our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability.
  arXiv  Detail & Related papers  (2023-10-06T02:35:35Z)
• Network Centralities in Quantum Entanglement Distribution due to User Preferences [5.243460995467895]
  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.
  arXiv  Detail & Related papers  (2023-08-16T07:00:09Z)
• On the Bipartite Entanglement Capacity of Quantum Networks [9.377912974064227]
  We consider multi-path entanglement distribution to a pair of nodes in a quantum network consisting of devices with non-deterministic entanglement swapping capabilities. We propose a mixed-integer quadratically constrained program (MIQCP) to solve this flow problem. We then compute the overall network capacity, defined as the maximum number of EPR states distributed to users per time unit.
  arXiv  Detail & Related papers  (2023-07-10T10:58:02Z)
• Block belief propagation algorithm for two-dimensional tensor networks [0.0]
  We propose a block belief propagation algorithm for contracting two-dimensional tensor networks and approximating the ground state of $2D$ systems. As applications, we use our algorithm to study the $2D$ Heisenberg and transverse Ising models, and show that the accuracy of the method is on par with state-of-the-art results.
  arXiv  Detail & Related papers  (2023-01-14T07:37:08Z)
• Unified Field Theory for Deep and Recurrent Neural Networks [56.735884560668985]
  We present a unified and systematic derivation of the mean-field theory for both recurrent and deep networks. We find that convergence towards the mean-field theory is typically slower for recurrent networks than for deep networks. Our method exposes that Gaussian processes are but the lowest order of a systematic expansion in $1/n$.
  arXiv  Detail & Related papers  (2021-12-10T15:06:11Z)
• Optimized Quantum Networks [68.8204255655161]
  Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests. We utilize this to design entanglement-based quantum networks tailored to their desired functionality.
  arXiv  Detail & Related papers  (2021-07-21T18:00:07Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)
• Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning [33.450042829375086]
  Multi-layered network exploration (MuLaNE) problem is an important problem abstracted from many applications. In MuLaNE, there are multiple network layers where each node has an importance weight and each layer is explored by a random walk. We systematically study this problem from offline optimization to online learning.
  arXiv  Detail & Related papers  (2021-06-09T13:35:39Z)
• 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)
• Multi-Level Local SGD for Heterogeneous Hierarchical Networks [11.699472346137739]
  We propose Multi-Level Local SGD, a distributed gradient method for a learning, non- objective framework in a heterogeneous network. We first provide a unified mathematical that describes the Multi-Level Local SGD algorithm. We then present a theoretical analysis of the algorithm.
  arXiv  Detail & Related papers  (2020-07-27T19:14:23Z)
• Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks [61.30171206892684]
  We develop a hybrid of intra- and inter-layer model learning that considers the network as a multi-layer cluster-based structure. MH-FL considers the topology structures among the nodes in the clusters, including local networks formed via device-to-device (D2D) communications. It orchestrates the devices at different network layers in a collaborative/cooperative manner to form local consensus on the model parameters.
  arXiv  Detail & Related papers  (2020-07-18T20:03:07Z)

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.