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

TL;DR

The paper addresses translating quantum computing advances into real-world industrial problems in ML and optimization during the NISQ era. It presents an editorial synthesis of a special issue, summarizing five contributions that span QC-based ML, quantum-inspired optimization, and hardware roadmaps. Key findings include rock-block stability prediction with QML outperforming classical models; QSVM-based movie recommendations with high accuracy on IBM hardware; VQA surrogates for phase-field metal solidification; QPSO-ELM intrusion detection with superior metrics; and a forward-looking review of IBM's hardware roadmap and D-Wave's scalability plans. The work demonstrates practical progress toward quantum utility in industry and outlines remaining challenges in scaling, fault tolerance, and integration with classical systems, while highlighting strategies to advance toward modular quantum processors by 2033.

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.