Sensitivity versus selectivity in entanglement detection via collective witnesses

TL;DR

This paper introduces a machine learning approach to design collective entanglement witnesses that can be tuned for sensitivity and selectivity, improving entanglement detection in quantum systems.

Contribution

It presents a novel supervised learning method for creating adaptable entanglement witnesses with enhanced detection capabilities.

Findings

Machine-designed witnesses enable continuous tuning of sensitivity and selectivity.

The approach requires fewer measurements than quantum tomography.

Potential for improved performance over traditional entanglement witnesses.

Abstract

In this paper, we present a supervised learning technique that utilizes artificial neural networks to design new collective entanglement witnesses for two-qubit and qubit-qutrit systems. Machine-designed collective entanglement witnesses allow for continuous tuning of their sensitivity and selectivity. These witnesses are, thus, a conceptually novel instrument allowing to study the sensitivity vs. selectivity trade-off in entanglement detection. The chosen approach is also favored due to its high generality, lower number of required measurements compared to quantum tomography, and potential for superior performance with regards to other types of entanglement witnesses. Our findings could pave the way for the development of more efficient and accurate entanglement detection methods in complex quantum systems, especially considering realistic experimental imperfections.