Exploring the application of quantum technologies to industrial and real-world use cases

TL;DR

This paper reviews recent progress in quantum computing's practical applications, highlighting advancements in hardware, algorithms, and hybrid schemes that aim to solve real-world problems in machine learning and optimization.

Contribution

It provides an overview of recent developments and potential of quantum technologies in industrial and real-world applications, emphasizing hybrid schemes and hardware improvements.

Findings

Quantum devices are advancing with larger qubit interconnections.

Hybrid quantum-classical schemes are simplifying algorithm development.

Quantum computing shows promise for machine learning and optimization.

Abstract

Recent advancements in quantum computing are leading to an era of practical utility, enabling the tackling of increasingly complex problems. The goal of this era is to leverage quantum computing to solve real-world problems in fields such as machine learning, optimization, and material simulation, using revolutionary quantum methods and machines. All this progress has been achieved even while being immersed in the noisy intermediate-scale quantum era, characterized by the current devices' inability to process medium-scale complex problems efficiently. Consequently, there has been a surge of interest in quantum algorithms in various fields. Multiple factors have played a role in this extraordinary development, with three being particularly noteworthy: (i) the development of larger devices with enhanced interconnections between their constituent qubits, (ii) the development of specialized frameworks, and (iii) the existence of well-known or ready-to-use hybrid schemes that simplify the method development process. In this context, this manuscript presents and overviews some recent contributions within this paradigm, showcasing the potential of quantum computing to emerge as a significant research catalyst in the fields of machine learning and optimization in the coming years.