Quantum machine learning: from physics to software engineering

Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key approaches that can offer advancements in both the development of quantum technologies and the power of artificial intelligence. Among these approaches are quantum-enhanced algorithms, which apply quantum software engineering to classical information processing to improve keystone machine learning solutions. In this context, we explore the capability of hybrid quantum-classical neural networks to improve model generalization and increase accuracy while reducing computational resources. We also illustrate how machine learning can be used both to mitigate the effects of errors on presently available noisy intermediate-scale quantum devices, and to understand quantum advantage via an automatic study of quantum walk processes on graphs. In addition, we review how quantum hardware can be enhanced by applying machine learning to fundamental and applied physics problems as well as quantum tomography and photonics. We aim to demonstrate how concepts in physics can be translated into practical engineering of machine learning solutions using quantum software.

Automated summary

Quantum machine learning is a rapidly growing field that combines quantum technology and artificial intelligence. This review provides an overview of key approaches that can advance quantum technology development and enhance the power of artificial intelligence. It discusses quantum-enhanced algorithms, hybrid quantum-classical neural networks, error mitigation on noisy quantum devices, quantum advantage through the study of quantum walk processes, and the application of machine learning to enhance quantum hardware. The aim is to show how physics concepts can be practically translated into machine learning solutions using quantum software.