Multiqubit entanglement of a general input state

TL;DR

This paper introduces a dynamic learning-based experimental method for estimating entanglement in multiqubit states, applicable to pure and mixed states without prior reconstruction, showing promising results for larger systems.

Contribution

The paper presents a novel, scalable entanglement estimation technique using dynamic learning that bypasses the need for state reconstruction or optimization.

Findings

Accurate entanglement estimates for three-qubit states.

Method extends easily to four- and five-qubit systems.

Training effort decreases as system size increases.

Abstract

Measurement of entanglement remains an important problem for quantum information. We present the design and simulation of an experimental method for entanglement estimation for a general multiqubit state. The system can be in a pure or a mixed state, and it need not be "close" to any particular state. Our method, based on dynamic learning, does not require prior state reconstruction or lengthy optimization. Results for three-qubit systems compare favorably with known entanglement measures. The method is then extended to four- and five-qubit systems, with relative ease. As the size of the system grows the amount of training necessary diminishes, raising hopes for applicability to large computational systems.