Scalable entanglement distribution framework for hybrid quantum networks

The realization of a scalable quantum network (QN) hinges on the efficient distribution of entanglement across heterogeneous hardware platforms. While traditional QN frameworks are often restricted to single-encoding schemes, relying exclusively on either discrete (DV) or continuous (CV) variables, such segregated architectures limit the capacity and interoperability of large-scale systems. Here, we propose a scalable hybrid entanglement distribution scheme that bridges these domains by mapping hybrid entanglement swapping and concentration at DV--CV interfaces onto a unified set of series and parallel graph rules. We demonstrate that our hybrid approach exhibits a {potential} performance advantage over traditional point-to-point generation of maximally entangled states, achieving enhanced end-to-end entanglement within series-parallel topologies. These results provide a theoretical foundation for designing resource-efficient, interpretable architectures for the future quantum internet.