  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.
