Entanglement Classification in the Graph States: The generalization to $n$-Qubits States using the Entanglement Matrix

TL;DR

This paper introduces the Entanglement Matrix formalism to classify and quantify entanglement in n-qubit graph states, revealing quadratic relationships and configuration-dependent behaviors that advance understanding of multi-qubit entanglement structures.

Contribution

The work develops a systematic entanglement classification method for n-qubit graph states using the Entanglement Matrix and graph theory, providing new analytical formulas for maximum entanglement.

Findings

Entanglement scales quadratically with qubit number.

Odd and even qubit systems exhibit distinct entanglement behaviors.

States with qubits multiple of 12 show enhanced entanglement.

Abstract

Graph states represent a significant class of multi-partite entangled quantum states with applications in quantum error correction, quantum communication, and quantum computation. In this work, we introduce a novel formalism called the Entanglement Matrix for quantifying and classifying entanglement in n-qubit graph states. Leveraging concepts from graph theory and quantum information, we develop a systematic approach to analyze entanglement by identifying primary and secondary midpoints in graph representations, where midpoints correspond to controlled-Z gate operations between qubits. Using Von Neumann entropy as our measure, we derive precise mathematical relationships for maximum entanglement in graph states as a function of qubit number. Our analysis reveals that entanglement follows a quadratic relationship with the number of qubits, but with distinct behaviors for odd versus even qubit systems. For odd n-qubit graph states, maximum entanglement follows $E_{\max} = n^2 - n$, while even n-qubit states exhibit higher entanglement with varying formulae depending on specific configurations. Notably, systems with qubit counts that are multiples of 12 demonstrate enhanced entanglement properties. This comprehensive classification framework provides valuable insights into the structure of multi-qubit entanglement, establishing an analytical foundation for understanding entanglement distribution in complex quantum systems that may inform future quantum technologies.