Disentanglement in dephasing channel with machine learning

TL;DR

This paper explores machine learning techniques to classify quantum states and quantify entanglement in two-qubit systems affected by dephasing noise, addressing limitations of traditional methods and general ANNs.

Contribution

It introduces specialized ANN algorithms optimized for noisy environments, improving classification and entanglement quantification with limited data.

Findings

Specialized ANNs outperform general models in noisy conditions

Effective entanglement quantification with fewer tomographic features

Limitations of traditional ANNs in dephasing noise scenarios

Abstract

Quantum state classification and entanglement quantification are of significant importance in the fundamental research of quantum information science and various quantum applications. Traditional methods, such as quantum state tomography, face exponential measurement demands with increasing numbers of qubits, necessitating more efficient approaches. Recent work has shown promise in using artificial neural networks (ANNs) for quantum state analysis. However, existing ANNs may falter when confronted with states affected by dephasing noise, especially with limited data and computational resources. In this study, we employ a machine-learning approach to investigate the disentanglement process in two-qubit systems in the presence of dephasing noise. Our findings highlight the limitations of general state-trained ANNs in classifying states under dephasing noise. Specialized ANN algorithms, tailored for classifying states and quantifying entanglement in such noisy environments, demonstrate excellent performance using only a subset of tomographic features.