Aspects of Configuring Weighted Cluster States Under Resource Constraints

This work studies optimization of cluster state configurations under resource constraints. Taking into account the finite degree of oscillator squeezing, we proposed a way of distributing the weight coefficients of a cluster state for maximizing pairwise entanglement in the cluster. We found constraints imposed on the cluster weight coefficients, as well as on the squeezing degree. In addition, we proposed a method that uses the centrality measure from social network theory for finding the optimal distribution of weight coefficients in a cluster state.