SPARQ: Efficient Entanglement Distribution and Routing in Space-Air-Ground Quantum Networks

TL;DR

This paper introduces SPARQ, a space-air-ground quantum network, and proposes a deep reinforcement learning-based entanglement routing method that significantly improves fidelity and efficiency over existing approaches.

Contribution

The paper develops a novel SPARQ network model and a deep RL framework for entanglement routing, addressing mobility and heterogeneity challenges in quantum networks.

Findings

TPED policy increases entanglement fidelity by 3% and reduces memory use by 50%.

DQN improves teleportation requests resolution by 39% and fidelity by 2%.

Compared to benchmarks, the approach enhances fidelity by up to 9%.

Abstract

In this paper, a space-air-ground quantum (SPARQ) network is developed as a means for providing a seamless on-demand entanglement distribution. The node mobility in SPARQ poses significant challenges to entanglement routing. Existing quantum routing algorithms focus on stationary ground nodes and utilize link distance as an optimality metric, which is unrealistic for dynamic systems like SPARQ. Moreover, in contrast to the prior art that assumes homogeneous nodes, SPARQ encompasses heterogeneous nodes with different functionalities further complicates the entanglement distribution. To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. Subsequently, an entanglement distribution policy, third-party entanglement distribution (TPED), is proposed to establish entanglement between communication parties. A realistic quantum network simulator is designed for performance evaluation. Simulation results show that the TPED policy improves entanglement fidelity by 3% and reduces memory consumption by 50% compared with benchmark. The results also show that the proposed DQN algorithm improves the number of resolved teleportation requests by 39% compared with shortest path baseline and the entanglement fidelity by 2% compared with an RL algorithm that is based on long short-term memory (LSTM). It also improved entanglement fidelity by 6% and 9% compared with two state-of-the-art benchmarks. Moreover, the entanglement fidelity is improved by 15% compared with DQN trained on a snapshot of SPARQ. Additionally, SPARQ enhances the average entanglement fidelity by 23.5% compared with existing networks spanning only space and ground layers.