General Machine Learning Algorithm for Quantum Teleportation

TL;DR

This paper introduces a machine learning-based algorithm that designs optimal quantum teleportation protocols across various systems, demonstrating quantum advantage and flexibility in fidelity and computational cost.

Contribution

It presents a versatile machine learning algorithm capable of creating optimal unitary operators for quantum teleportation in diverse quantum systems.

Findings

Demonstrates quantum advantage over classical schemes.

Applies to single and multiple qubit states, coherent and Dicke states.

Shows flexibility in balancing fidelity and computational resources.

Abstract

We present a general algorithm, based on machine learning, which can create optimal unitary operators to implement quantum teleportation in any system with well-defined set of measurements in a relevant entangled basis. We illustrate it with a collective spin model and demonstrate its versatility by applying it to teloportation of single and multiple qubit states, coherent and Dicke states, and for systems with prior distributions and unequal dimensions. All cases display significant regimes of quantum advantage over corresponding classical schemes with no entanglement. The algorithm offers the flexibility to choose a balance between target fidelity and computational cost.