High-dimentional Multipartite Entanglement Structure Detection with Low Cost

TL;DR

This paper introduces a neural network-based method for detecting complex multipartite entanglement structures in large quantum systems with significantly reduced measurement costs, achieving high accuracy up to 19 qubits.

Contribution

A novel multi-view neural network approach that reduces measurement complexity from exponential to polynomial for entanglement detection in large-scale quantum systems.

Findings

Over 95% detection accuracy for systems with up to 19 qubits

Measurement cost scales polynomially with qubit number

Enables resource-efficient entanglement analysis in large quantum states

Abstract

Quantum entanglement detection and characterization are crucial for various quantum information processes. Most existing methods for entanglement detection rely heavily on a complete description of the quantum state, which requires numerous measurements and complex setups. This makes these theoretically sound approaches costly and impractical, as the system size increases. In this work, we propose a multi-view neural network model to generate representations suitable for entanglement structure detection. The number of required quantum measurements is polynomial rather than exponential increase with the qubit number. This remarkable reduction in resource costs makes it possible to detect specific entanglement structures in large-scale systems. Numerical simulations show that our method achieves over 95% detection accuracy for up to 19 qubits systems. By enabling a universal, flexible and resource-efficient analysis of entanglement structures, our approach enhances the capability of utilizing quantum states across a wide range of applications.