论文标题

量子网络中的纠缠前分布

Pre-Distribution of Entanglements in Quantum Networks

论文作者

Ghaderibaneh, Mohammad, Gupta, Himanshu, Ramakrishnan, C. R., Luo, Ertai

论文摘要

量子网络通信具有挑战性,因为量子制度中的无关定理使许多古典技术无法应用。对于长距离通信,唯一可行的方法是量子状态的传送,该量子状态需要先前分布纠缠的Qubits(EP)。由于基础物理过程成功的可能性很低,因此在远程节点上建立EP会产生明显的潜伏期。为了减少EP的生成潜伏期,先前的工作已经研究了有效的纠缠路线路径的选择,并同时使用多个此类途径进行EP生成。在本文中,我们提出并研究了一种补充技术,以减少EP生成潜伏期 - 与某些(预定的)对网络节点对分发EPS(预分发EP);然后,这些预分布的EPS可在需要时使用较低的生成潜伏期来为请求的对生成EP。为了使分发前的方法最有效,我们需要解决选择节点对选择的优化问题,其中应预先分发EPS,以最大程度地减少预期EP请求的生成潜伏期,并在给定的成本限制下。在本文中,我们适当地提出了上述优化问题,并设计了两种有效的算法,其中一种是一种基于特殊情况的近似算法的贪婪方法。通过对NetSquid模拟器的广泛评估,我们证明了我们的方法和开发技术的有效性。我们表明,我们所开发的算法的表现优于一种天真的方法,最多可达一个数量级。

Quantum network communication is challenging, as the No-Cloning theorem in quantum regime makes many classical techniques inapplicable. For long-distance communication, the only viable approach is teleportation of quantum states, which requires a prior distribution of entangled pairs (EPs) of qubits. Establishment of EPs across remote nodes can incur significant latency due to the low probability of success of the underlying physical processes. To reduce EP generation latency, prior works have looked at selection of efficient entanglement-routing paths and simultaneous use of multiple such paths for EP generation. In this paper, we propose and investigate a complementary technique to reduce EP generation latency--to pre-distribute EPs over certain (pre-determined) pairs of network nodes; these pre-distributed EPs can then be used to generate EPs for the requested pairs, when needed, with lower generation latency. For such an pre-distribution approach to be most effective, we need to address an optimization problem of selection of node-pairs where the EPs should be pre-distributed to minimize the generation latency of expected EP requests, under a given cost constraint. In this paper, we appropriately formulate the above optimization problem and design two efficient algorithms, one of which is a greedy approach based on an approximation algorithm for a special case. Via extensive evaluations over the NetSquid simulator, we demonstrate the effectiveness of our approach and developed techniques; we show that our developed algorithms outperform a naive approach by up to an order of magnitude.

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