Beyond Bell Teleportation: Machine-Learned Adaptive Protocols

TL;DR

This paper introduces a machine-learned adaptive protocol to enhance quantum teleportation fidelity under various noise conditions, surpassing traditional Bell teleportation.

Contribution

It presents a novel machine learning-based adaptive scheme for quantum teleportation that improves performance across different noise models, demonstrating its effectiveness and flexibility.

Findings

Significant fidelity improvements over classical Bell teleportation in noisy environments.

The protocol adapts to different noise types, including bit-flip, amplitude damping, and depolarizing.

Reveals strategies for decoherence compensation and information loss mitigation.

Abstract

Quantum teleportation have a central role in quantum information science and allows transferring of an unknown quantum state through entanglement and classical communication. Unfortunately, the interaction with external and internal noise severely affects the quality of teleportation and poses limitations on practical applications of quantum communication networks. In this work, instead of conventional Bell teleportation, we introduce a Machine Learned adaptive protocol for optimizing multiple components of Quantum Teleportation in order to achieve higher fidelity in various noise environments. In order to demonstrate the performance of the proposed scheme, we study three different noise models, including bit-flip, amplitude damping, and depolarizing noise, both in case of single-qubit and two-qubit channels. As a result, we observe substantial improvement in the teleportation fidelity in comparison to the classical Bell-state teleportation protocol in certain noise conditions. Furthermore, the machine-learned protocol reveals a nontrivial strategy for compensation of decoherence and information losses. In addition, obtained results indicate the flexibility and reliability of the proposed framework for implementing various adaptive quantum communications while shedding light on possibilities of discovery of optimal quantum algorithms by means of automated approache