Introducing the Correlation Concentration Ratio (CCR): Quantitative Framework for Comparing Quantum Cluster States

TL;DR

This paper introduces the CCR metric to quantitatively compare the structural entanglement of different quantum cluster state topologies in measurement-based quantum computation.

Contribution

The paper presents the CCR metric as a novel quantitative tool for analyzing and comparing the correlation structures of quantum cluster states.

Findings

Simulation of four-mode cluster states with various topologies shows correlation patterns match theoretical nullifiers.

Increasing squeezing enhances target correlations and suppresses unwanted anti-squeezing effects.

CCR effectively quantifies and compares the structural entanglement of different cluster topologies.

Abstract

In this paper, numerical simulations of four-mode continuous-variable cluster states with different topologies in the framework of measurement-based quantum computation are presented. By utilizing the symplectic representation and covariance matrix, the process of generating cluster states with linear, square, and T-shaped topologies has been systematically modeled. The simulation results show that the cluster graph structure is directly reflected in the pattern of quadrature correlations; in other words, the theoretical nullifier relations of the cluster states are reproduced in the final covariance matrices. Increasing the squeezing parameter leads to the strengthening of the target correlations and the suppression of unwanted components arising from anti-squeezing; such that the off-diagonal elements of the covariance matrix in the linear and square topologies increase to significant values, and in the T-shaped topology a stronger central correlation (similar to GHZ-like behavior in the continuous-variable domain) is observed. In order to quantitatively analyze these structural differences, a metric titled CCR (Correlation Concentration Ratio) is introduced that quantifies the concentration of effective correlations on the graph edges relative to the total correlations of the system. This index enables direct comparison of different topologies from the perspective of structural entanglement distribution and provides a framework for evaluating the efficiency of cluster graphs in MBQC architectures. The results show that CCR can be used as a practical tool for designing and selecting optimal topologies in larger clusters and more complex structures.