Quantum Machine Learning for Industry 4.0
Indu Bala, Kiran Ahuja, Maad Mohsin Mijwil · 2025
The convergence of quantum computing and machine learning holds promise for revolutionizing the manufacturing sector by enhancing production efficiency, cutting costs, and elevating product quality. This chapter delves into the burgeoning realm of quantum machine learning (QML) and its implications within the framework of Industry 4.0. Initially, it provides an overview of quantum computing and machine learning, delineating their respective merits and limitations. Subsequently, it explores the synergies between these domains and the obstacles accompanying their integration. The chapter elucidates the practical applications of QML in Industry 4.0, encompassing predictive maintenance, anomaly detection, supply chain optimization, and quality assurance. It elucidates how QML aids in preempting equipment breakdowns, thereby curbing maintenance expenses and downtime, and identifies production irregularities to bolster efficiency and diminish wastage. The chapter underscores the paramount significance of data security and privacy in deploying QML within Industry 4.0. It scrutinizes the vulnerabilities stemming from quantum computing's potential to breach conventional encryption methods, underscoring the imperative for novel security protocols to safeguard sensitive information. Ultimately, the chapter reflects on the current landscape of QML research in Industry 4.0 and contemplates future trajectories. The authors advocate for expanded inquiry and experimentation to fully grasp QML's capacities and constraints in Industry 4.0. Furthermore, they envision forthcoming applications of QML in domains such as autonomous manufacturing, bespoke production, and adaptable supply chains. In essence, this chapter furnishes a holistic examination of QML's emergence and its conceivable ramifications in Industry 4.0. It underscores the necessity for continued research and development in this sphere while underscoring the criticality of data security and privacy in QML's industrial implementation. This scholarly discourse is poised to captivate researchers, industry practitioners, and policymakers invested in advancing and integrating Industry 4.0 technologies.