Statistical Characterization of Entanglement Degradation Under Markovian Noise in Composite Quantum Systems

TL;DR

This paper introduces a statistical method to analyze how Markovian noise affects entanglement in high-dimensional quantum systems, revealing differences between global and local noise impacts on entanglement longevity.

Contribution

It presents a computational approach using the Cao and Lu method to efficiently simulate entanglement degradation under various noise models in composite quantum systems.

Findings

Global noise leads to longer entanglement persistence.

Local noise results in shorter entanglement lifetime.

The method efficiently handles systems up to dimension 8.

Abstract

Understanding how noise degrades entanglement is crucial for the development of reliable quantum technologies. While the Markovian approximation simplifies the analysis of noise, it remains computationally demanding, particularly for high-dimensional systems like quantum memories. In this paper, we present a statistical approach to study the impact of different noise models on entanglement in composite quantum systems. By comparing global and local noise scenarios, we quantify entanglement degradation using the Positive Partial Transpose Time (PPTT) metric, which measures how long entanglement persists under noise. When the sampling of different noise scenarios is performed under controlled and homogeneous conditions, our analysis reveals that systems subjected to global noise tend to exhibit longer PPTTs, whereas those influenced by independent local noise models display the shortest entanglement persistence. To carry out this analysis, we employ a computational method proposed by Cao and Lu, which accelerates the simulation of PPTT distributions and enables efficient analysis of systems with dimensions up to $D=8$. Our results demonstrate the effectiveness of this approach for investigating the resilience of quantum systems under Markovian noise.