A Heuristic Remote Entanglement Distribution Algorithm on Memory-Limited Quantum Paths
A Heuristic Remote Entanglement Distribution Algorithm on Memory-Limited Quantum Paths
复制标题
DOI：
10.1109/tcomm.2022.3205683
复制
发表时间：
2022-11
影响因子：
8.3
通讯作者：
Lutong Chen;Kaiping Xue;Jian Li;Nenghai Yu;Ruidong Li;Jianqing Liu;Qibin Sun;Jun Lu
中科院分区：
文献类型：
--
作者：
Lutong Chen;Kaiping Xue;Jian Li;Nenghai Yu;Ruidong Li;Jianqing Liu;Qibin Sun;Jun Lu
远程纠缠分发在大规模量子网络中起着至关重要的作用，而量子路由器（或中继器）是实现远程纠缠分发的关键使能器，它可以延长纠缠传输距离。然而，量子路由器的性能还远远不够完美。其中，量子路由器中有限的量子存储器在很大程度上影响了纠缠分发的速率和效率。为了克服这一挑战，本文提出了一种新的模型，最大化的纠缠分发率（EDR）的内存有限的路径，然后将其转化为纠缠产生和交换的子问题。我们提出了一种产生短距离纠缠的贪婪算法，以便有效地利用量子存储器。至于纠缠交换子问题，我们使用纠缠图（EG）对其进行建模，其解决方案至少是NP-完全的。鉴于此，我们提出了一种启发式算法，通过将原始EG划分为几个子问题，每个子问题都可以使用动态规划（DP）在多项式时间内求解。仿真结果表明，该方案具有较高的EDR，算法具有多项式时间上界和合理的平均运行时复杂度。
Remote entanglement distribution plays a crucial role in large-scale quantum networks, and the key enabler for entanglement distribution is quantum routers (or repeaters) that can extend the entanglement transmission distance. However, the performance of quantum routers is far from perfect yet. Amongst the causes, the limited quantum memories in quantum routers largely affect the rate and efficiency of entanglement distribution. To overcome this challenge, this paper presents a new modeling for the maximization of entanglement distribution rate (EDR) on a memory-limited path, which is then transformed into entanglement generation and swapping sub-problems. We propose a greedy algorithm for short-distance entanglement generation so that the quantum memories can be efficiently used. As for the entanglement swapping sub-problem, we model it using an Entanglement Graph (EG), whose solution is yet found to be at least NP-complete. In light of it, we propose a heuristic algorithm by dividing the original EG into several sub-problems, each of which can be solved using dynamic programming (DP) in polynomial time. By conducting simulations, the results show that our proposed scheme can achieve a high EDR, and the developed algorithm has a polynomial-time upper bound and reasonable average runtime complexity.