Entanglement Distribution in Satellite-based Dynamic Quantum Networks
- URL: http://arxiv.org/abs/2306.08894v1
- Date: Thu, 15 Jun 2023 06:56:26 GMT
- Title: Entanglement Distribution in Satellite-based Dynamic Quantum Networks
- Authors: Alena Chang, Yinxin Wan, Guoliang Xue, Arunabha Sen
- Abstract summary: Low Earth Orbit (LEO) satellites present a compelling opportunity for the establishment of a global quantum information network.
Existing works often do not account for satellite movement over time when distributing entanglement and/or often do not permit entanglement distribution along inter-satellite links.
We first define a system model which considers both satellite movement over time and inter-satellite links.
- Score: 10.445684354981847
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low Earth Orbit (LEO) satellites present a compelling opportunity for the
establishment of a global quantum information network. However, satellite-based
entanglement distribution from a networking perspective has not been fully
investigated. Existing works often do not account for satellite movement over
time when distributing entanglement and/or often do not permit entanglement
distribution along inter-satellite links, which are two shortcomings we address
in this paper. We first define a system model which considers both satellite
movement over time and inter-satellite links. We next formulate the optimal
entanglement distribution (OED) problem under this system model and show how to
convert the OED problem in a dynamic physical network to one in a static
logical graph which can be used to solve the OED problem in the dynamic
physical network. We then propose a polynomial time greedy algorithm for
computing satellite-assisted multi-hop entanglement paths. We also design an
integer linear programming (ILP)-based algorithm to compute optimal solutions
as a baseline to study the performance of our greedy algorithm. We present
evaluation results to demonstrate the advantage of our model and algorithms.
Related papers
- Efficient Entanglement Routing for Satellite-Aerial-Terrestrial Quantum Networks [28.392847313513503]
Space-aerial-terrestrial quantum networks (SATQNs) are shaping the future of the global-scale quantum Internet.
This paper investigates the collaboration among satellite, aerial, and terrestrial quantum networks to efficiently transmit high-fidelity quantum entanglements over long distances.
arXiv Detail & Related papers (2024-09-20T13:57:32Z) - A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system.
We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks.
The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem.
arXiv Detail & Related papers (2024-08-29T06:57:45Z) - Quantum Annealing-Based Algorithm for Efficient Coalition Formation Among LEO Satellites [4.737806718785056]
As the number of satellites increases, the number of communication links to maintain also rises.
This paper formulates the clustering of LEO satellites as a coalition structure generation (CSG) problem.
We obtain the optimal partitions using a hybrid quantum-classical algorithm called GCS-Q.
Our experiments, conducted using the D-Wave Advantage annealer and the state-of-the-art solver Gurobi, demonstrate that the quantum annealer significantly outperforms classical methods in terms of runtime.
arXiv Detail & Related papers (2024-08-12T08:53:46Z) - Scalable Scheduling Policies for Quantum Satellite Networks [10.91414940065524]
We consider the problem of transmission scheduling in quantum satellite networks subject to resource constraints at the satellites and ground stations.
We show that the most general problem of assigning satellites to ground station pairs for entanglement distribution is NP-hard.
We propose four scalable algorithms and evaluate their performance for Starlink mega constellation.
arXiv Detail & Related papers (2024-05-15T15:58:12Z) - Satellite Federated Edge Learning: Architecture Design and Convergence Analysis [47.057886812985984]
This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to mega-constellation networks.
By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL.
Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation.
arXiv Detail & Related papers (2024-04-02T11:59:58Z) - Toward Autonomous Cooperation in Heterogeneous Nanosatellite
Constellations Using Dynamic Graph Neural Networks [0.0]
The paper proposes a novel approach to overcome the challenges by modeling the constellations and CP as dynamic networks.
The trained neural network can predict the network delay with a mean absolute error of 3.6 minutes.
Simulation results show that the proposed method can successfully design a contact plan for large satellite networks, improving the delay by 29.1%, similar to a traditional approach.
arXiv Detail & Related papers (2024-03-01T17:26:02Z) - Learning Emergent Random Access Protocol for LEO Satellite Networks [51.575090080749554]
We propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH)
eRACH is a model-free approach that emerges through interaction with the non-stationary network environment.
Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput.
arXiv Detail & Related papers (2021-12-03T07:44:45Z) - SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots [112.2491765424719]
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal.
We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints.
recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution.
A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
arXiv Detail & Related papers (2021-08-03T02:56:21Z) - Learning to Beamform in Heterogeneous Massive MIMO Networks [48.62625893368218]
It is well-known problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks.
We propose a novel deep learning based paper algorithm to address this problem.
arXiv Detail & Related papers (2020-11-08T12:48:06Z) - Graph Neural Networks for Motion Planning [108.51253840181677]
We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems.
We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT)
Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
arXiv Detail & Related papers (2020-06-11T08:19:06Z)
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.