In this study, we develop a resource management framework for a quantum virtual private network (qVPN), which involves the sharing of an underlying public quantum network by multiple organizations for quantum entanglement distribution. Our approach involves resolving the issue of link entanglement resource allocation in a qVPN by utilizing a centralized optimization framework. We provide insights into the potential of genetic and learning-based algorithms for optimizing qVPNs, and emphasize the significance of path selection and distillation in enabling efficient and reliable quantum communication in multi-organizational settings. Our findings demonstrate that compared to traditional greedy based heuristics, genetic and learning-based algorithms can identify better paths. Furthermore, these algorithms can effectively identify good distillation strategies to mitigate potential noises in gates and quantum channels, while ensuring the necessary quality of service for end users.

翻译：在本研究中，我们为量子虚拟专用网络（qVPN）开发了一个资源管理框架，该框架涉及多个组织共享底层公共量子网络以实现量子纠缠分发。我们的方法通过利用集中式优化框架来解决qVPN中的链路纠缠资源分配问题。我们探讨了遗传算法和基于学习的算法在优化qVPN方面的潜力，并强调了路径选择与蒸馏在多组织环境中实现高效可靠量子通信的重要性。研究结果表明，与传统基于贪心策略的启发式算法相比，遗传算法和基于学习的算法能够识别出更优的路径。此外，这些算法还能有效制定良好的蒸馏策略，以减轻量子门和量子信道中潜在噪声的影响，同时确保最终用户获得必要的服务质量。