Entanglement-Based Artificial Topology: Neighboring Remote Network Nodes
Si-Yi Chen, Jessica Illiano, Angela Sara Cacciapuoti, Marcello Caleffi
TL;DR
This work reframes inter-network connectivity in quantum networks by leveraging multipartite entanglement to create a dynamically adjustable artificial topology between two QLANs. The authors propose a binary star graph state $|S_{n_1,n_2}\rangle$ distributed across the two QLANs, built from intra-QLAN star states and a single inter-QLAN EPR, enabling inter-QLAN links via local operations only. They classify several traffic-driven artificial topologies—hierarchical peer-to-peer, pure peer-to-peer, role delegation, and extranet—demonstrating that these LU-equivalent graphs can be realized by local Pauli measurements and single-qubit gates without additional quantum communication. This approach provides a proactive, traffic-aware connectivity paradigm that can adapt to varying demands, potentially improving inter-QLAN communication while maintaining low physical-resource requirements. The work also outlines future directions on noise resilience, scalability, and simulation in realistic network scenarios.
Abstract
Entanglement is unanimously recognized as the key communication resource of the Quantum Internet. Yet, the possibility of implementing novel network functionalities by exploiting the marvels of entanglement has been poorly investigated so far, by mainly restricting the attention to bipartite entanglement. Conversely, in this paper, we aim at exploiting multipartite entanglement as inter-network resource. Specifically, we consider the interconnection of different Quantum Local Area Networks (QLANs), and we show that multipartite entanglement allows to dynamically generate an inter-QLAN artificial topology, by means of local operations only, that overcomes the limitations of the physical QLAN topologies. To this aim, we first design the multipartite entangled state to be distributed within each QLAN. Then, we show how such a state can be engineered to: i) interconnect nodes belonging to different QLANs, and ii) dynamically adapt to different inter-QLAN traffic patterns. Our contribution aims at providing the network engineering community with a hands-on guideline towards the concept of artificial topology and artificial neighborhood.
