A Review of the Applications of Quantum Machine Learning in Optical Communication Systems

TL;DR

This paper reviews how quantum and quantum-inspired machine learning algorithms can enhance optical communication systems, especially in error correction, by leveraging their potential advantages over classical methods.

Contribution

It provides a comprehensive survey of existing quantum machine learning algorithms and evaluates their applicability to optical signal processing.

Findings

Quantum ML algorithms show promise for improved error correction.

Current technology enables some quantum ML applications in optics.

Quantum-inspired methods offer practical benefits in optical systems.

Abstract

In the context of optical signal processing, quantum and quantum-inspired machine learning algorithms have massive potential for deployment. One of the applications is in error correction protocols for the received noisy signals. In some scenarios, non-linear and unknown errors can lead to noise that bypasses linear error correction protocols that optical receivers generally implement. In those cases, machine learning techniques are used to recover the transmitted signal from the received signal through various estimation procedures. Since quantum machine learning algorithms promise advantage over classical algorithms, we expect that optical signal processing can benefit from these advantages. In this review, we survey several proposed quantum and quantum-inspired machine learning algorithms and their applicability with current technology to optical signal processing.