Entanglement estimation of Werner states with a quantum extreme learning machine

TL;DR

This paper introduces a quantum extreme learning machine protocol for estimating entanglement in Werner states, utilizing random state generation, Hamiltonian evolution, and observable-based training, with robustness and parameter influence analysis.

Contribution

It presents a novel QELM-based method for entanglement estimation in Werner states, incorporating noise robustness and Hamiltonian parameter analysis.

Findings

The protocol accurately estimates entanglement in Werner states.

Performance remains robust under noisy input conditions.

Magnetic field parameters influence estimation accuracy.

Abstract

Quantum Extreme Learning Machines (QELMs) have emerged as a potent tool for various quantum information processing tasks. We present a QELM protocol for estimating the amount of entanglement in Werner states. The protocol requires the generation of a sequence of random Werner states, which are then combined with a reservoir state and evolved using an Ising Hamiltonian. A set of observables based on the Bloch basis is constructed and employed to train the system to recognize unseen features. To assess the protocol's robustness, noise is introduced into the input states, and the system's performance under these noisy conditions is analyzed. Additionally, the influence of the magnetic field parameter within the Ising Hamiltonian on the estimation accuracy is investigated.