Adaptive Negativity Estimation via Collective Measurements

TL;DR

This paper presents an adaptive measurement approach combined with machine learning to efficiently quantify entanglement in small quantum systems, improving accuracy over non-adaptive methods.

Contribution

It introduces a novel adaptive measurement protocol utilizing LSTM networks for entanglement estimation in two-qubit and qubit-qutrit systems.

Findings

Adaptive measurements outperform non-adaptive strategies.

LSTM-based processing enhances inference precision.

Method reduces measurement resources needed.

Abstract

This paper explores an efficient method for entanglement quantification in two-qubit and qubit-qutrit quantum systems based upon the framework of collective measurements in conjunction with machine learning. We introduce an adaptive measurement procedure in which measurement settings are dynamically adjusted based on prior measurement outcomes aiming to optimize the inference precision given a limited number of these measurement settings. The procedure makes use of the Long Short-Term Memory networks to recurrently process collective measurements on two copies of the investigated states. Obtained results demonstrate the tangible benefits of the adaptive measurements in comparison to previously described non-adaptive strategies.