Data supporting the publication: On-Demand Resource Allocation for a Quantum Network Hub

Datasets supporting publication: On-Demand Resource Allocation for a Quantum Network Hub.Abstract: To effectively support the execution of quantum network applications for multiple sets of user-controlled quantum nodes, a quantum network must efficiently allocate shared resources. We study traffic models for a type of quantum network hub called an Entanglement Generation Switch (EGS), a device that allocates resources to enable entanglement generation between nodes in response to user-generated demand. We propose an on-demand resource allocation algorithm, where a demand is either blocked if no resources are available or else results in immediate resource allocation. We model the EGS as an Erlang loss system, with demands corresponding to sessions whose arrival is modeled as a Poisson process. To reflect the operation of a practical quantum switch, our model captures scenarios where a resource is allocated for batches of entanglement generation attempts, possibly interleaved with calibration periods for the quantum network nodes. Calibration periods are necessary to correct against drifts or jumps in the physical parameters of a quantum node that occur on a timescale that is long compared to the duration of an attempt. We then derive a formula for the demand blocking probability under three different traffic scenarios using analytical methods from applied probability and queueing theory. We prove an insensitivity theorem which guarantees that the probability a demand is blocked only depends upon the mean duration of each entanglement generation attempt and calibration period, and is not sensitive to the underlying distributions of attempt and calibration period duration. We provide numerical results to support our analysis. Our numerical results suggest that there exist parameter regimes where it is beneficial for nodes to relinquish control of EGS resources during their calibration periods. This benefit is quantified by the blocking probability and the total entanglement generated in a fixed period of time. Our work is the first analysis of traffic characteristics at an EGS system and provides a valuable analytic tool for devising performance driven resource allocation algorithms.

支撑发表论文《量子网络枢纽的按需资源分配》的数据集。摘要：为有效支撑多组用户管控量子节点的量子网络应用运行，量子网络需高效分配共享资源。我们针对一类被称为纠缠生成交换机（Entanglement Generation Switch，EGS）的量子网络枢纽展开流量模型研究，该设备可根据用户发起的需求分配资源，以实现节点间的纠缠生成。我们提出一种按需资源分配算法：若当前无可用资源，则需求将被阻塞；反之则可立即完成资源分配。我们将EGS建模为埃尔朗格损失系统（Erlang loss system），将需求对应为会话，其到达过程服从泊松过程（Poisson process）。为贴合实际量子交换机的运行逻辑，我们的模型涵盖以下场景：资源被分配用于多批次纠缠生成尝试，且该过程可能与量子网络节点的校准时段交替进行。校准时段是必要的，用于校正量子节点物理参数的漂移或跳变——这类参数变化的时间尺度远长于单次生成尝试的时长。随后我们借助应用概率论与排队论的分析方法，推导了三种不同流量场景下的需求阻塞概率公式。我们证明了一个无敏感性定理：该定理保证，需求阻塞概率仅取决于单次纠缠生成尝试与校准时段的平均时长，而与尝试及时长的底层分布无关。我们提供了数值仿真结果以支撑上述分析。数值仿真结果表明，存在若干参数区间，使得节点在校准时段内放弃对EGS资源的控制权将带来收益。该收益可通过阻塞概率与固定时段内生成的总纠缠数进行量化。本研究是首个针对EGS系统流量特性的分析工作，可为设计性能驱动型资源分配算法提供极具价值的分析工具。