Dynamic learning of pairwise and three-way entanglement

TL;DR

This paper introduces a dynamic learning approach to train a quantum computer with three qubits to accurately estimate pairwise and three-way entanglement, advancing quantum neural network capabilities.

Contribution

The paper presents a novel application of dynamic learning to a three-qubit system for entanglement estimation, extending previous work on quantum neural networks.

Findings

Successfully trained a three-qubit system to estimate entanglement

Demonstrated the method's ability to handle pairwise and three-way entanglement

Shows potential for scalable quantum entanglement measurement

Abstract

In previous work, we have developed a dynamic learning paradigm for "programming" a general quantum computer. A learning algorithm is used to find a set of parameters for a coupled qubit system such that the system at an initial time evolves to a state in which a given measurement results in the desired calculation value. This can be thought of as a quantum neural network (QNN). Here, we apply our method to a system of three qubits, and demonstrate training the quantum computer to estimate both pairwise and three-way entanglement.