Quantum Autoencoders for Learning Quantum Channel Codes

TL;DR

This paper presents a quantum machine learning framework using quantum autoencoders to generate and evaluate quantum channel codes across different models, demonstrating promising results for quantum communication.

Contribution

It introduces a novel quantum machine learning approach employing parameterized quantum circuits for designing quantum channel codes in various communication scenarios.

Findings

Strong performance in multiple quantum channel models

Potential to improve quantum communication system design

Insights into capacity bounds under different conditions

Abstract

This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible channel noise model, we develop a machine learning framework to generate quantum channel codes and evaluate their effectiveness. We explore classical, entanglement-assisted, and quantum communication scenarios within our framework. Applying it to various quantum channel models as proof of concept, we demonstrate strong performance in each case. Our results highlight the potential of quantum machine learning in advancing research on quantum communication systems, enabling a better understanding of capacity bounds under modulation constraints, various communication settings, and diverse channel models.