Adaptive Entanglement Generation for Quantum Routing

TL;DR

This paper introduces reinforcement learning models for quantum network routing that significantly improve speed and success rates of entanglement generation by proactive strategies and caching, outperforming traditional optimization methods.

Contribution

It presents a novel RL-based approach for quantum routing that is faster and more effective than linear programming, incorporating caching and proactive swapping strategies.

Findings

RL approach is 20x faster than linear programming

Caching entanglements improves routing performance by 10-15%

Proactive swapping increases success rate by up to 52.55%

Abstract

Entanglement generation in long-distance quantum networks is a difficult process due to resource limitations and the probabilistic nature of entanglement swapping. To maximize success probability, existing quantum routing algorithms employ computationally expensive solutions (e.g., linear programming) to determine which links to entangle and use for end-to-end entanglement generation. Such optimization methods, however, cannot meet the delay requirements of real-world quantum networks, necessitating swift yet efficient real-time optimization models. In this paper, we propose reinforcement learning (RL)-based models to determine which links to entangle and proactively swap to meet connection requests. We show that the proposed RL-based approach is 20x faster compared to linear programming. Moreover, we show that one can take advantage of the longevity of entanglements to (i) cache entangled links for future use and (ii) proactively swap entanglement on high-demand path segments, thereby increasing the likelihood of request success. Through comprehensive simulations, we demonstrate that caching unused entanglements leads to a 10-15% improvement in the performance of state-of-the-art quantum routing algorithms. Complementing caching with proactive entanglement swapping further enhances the request success rate by up to 52.55%.