Synergy of machine learning with quantum computing and communication

TL;DR

This paper reviews how machine learning techniques are applied to quantum computing and communication, highlighting current approaches, challenges, and future research directions in this interdisciplinary field.

Contribution

It provides a comprehensive overview of state-of-the-art ML methods in quantum computing and communication, identifying key challenges and potential research avenues.

Findings

Classical ML models are used for quantum error correction and cryptography.

The paper identifies current challenges in integrating ML with quantum systems.

Future research directions are proposed for advancing the field.

Abstract

Machine learning in quantum computing and communication provides intensive opportunities for revolutionizing the field of Physics, Mathematics, and Computer Science. There exists an aperture of understanding behind this interdisciplinary domain and a lack of core understanding renders an opportunity to explore the machine learning techniques for this domain. This paper gives a comprehensive review of state-of-the-art approaches in quantum computing and quantum communication in the context of Artificial Intelligence and machine learning models. The paper reviews the classical ML models that have been employed in various ways for quantum computation such as quantum error correction, quantum communication, quantum cryptography, and mapping quantum algorithms to the existing hardware. The paper also illustrates how the relevant current challenges can be transformed into future research avenues.