Efficient Routing on Quantum Networks using Adaptive Clustering

TL;DR

A comprehensive simulation-based evaluation shows QuARC is robust against changes to physical network parameters, and maintains high throughput without starvation even as network sizes scale and physical parameters degrade.

Abstract

We introduce QuARC, Quantum Adaptive Routing using Clusters, a novel clustering-based entanglement routing protocol that leverages redundant, multi-path routing through multi-particle projective quantum measurements to enable high-throughput, low-overhead, starvation-free entanglement distribution. At its core, QuARC periodically reconfigures the underlying quantum network into clusters of different sizes, where each cluster acts as a small network that distributes entanglement across itself, and the end-to-end entanglement is established by further distributing between clusters. QuARC does not require a-priori knowledge of any physical parameters, and is able to adapt the network configuration using static topology information, and using local (within-cluster) measurements only. We present a comprehensive simulation-based evaluation that shows QuARC is robust against changes to physical network parameters, and maintains high throughput without starvation even as network sizes scale and physical parameters degrade.

Figures (7)

• Figure 1: Comparison of different quantum routing approaches.
• Figure 2: Routing in QuARC. (a) The network is partitioned into clusters. Nodes are colored by the cluster they belong to. (b) Shortest path routing over clusters. (c) Cluster nodes assign qubits to channels, attempt link generation, and perform fusions to route entanglement from the source to the destination.
• Figure 3: QuARC Cluster reconfiguration: cluster A (red) opts to split, and cluster B (orange) opts to merge. Panel (d) shows the final configuration.
• Figure 4: Threshold calculation.
• Figure 5: QuARC adaptation with changing physical parameters ($p$ and $q$); (a-c) $16\times16$ grid network. (a) Sudden parameter shifts. (b) Gradual parameter decay. (c) Sharp parameter oscillation. (d) Spatially-varying parameters.