Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.
Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.
We explore the applications of quantum computers and algorithms in the Industrial Internet of Things (IioT) sector and provide a unique quantum technique to assure the safety and reliability of wireless network device decision-making. The suggested quantum system is shown to be adaptable and versatile in two case examples. The quantum optimization algorithm (QOA) solves complex transportation and shipping problems in smart industrial logistic systems in the first case study. A financial sector quantum neural network (Q-NN) efficiently handles price-related data for smart factory forecasts and opportunity identification in the second case study. The suggested quantum system, including device, edge, fog, and cloud layers, gathers, assesses, and uses quantum-enhanced data for decision-making to simulate and optimize smart factories across sectors. Quantum technology’s revolutionary potential in information processing, secure data transport, and quantum machine learning is also studied. Case studies show that the quantum method is feasible and effective, suggesting sector-changing reforms. The findings show that quantum technology will improve optimization, machine learning, and data analysis, ushering in a new computer era. IBM Quantum Lab’s Qiskit software experiments show that these quantum algorithms are implemented and perform well, enabling the integration of compact quantum computers at the edge to improve smart factory scalability, quality, and cost-effectiveness while establishing self-learning optimized architectures.
Abstract The amount and difficulty of data have both grown a lot since the number of Internet of Things (IoT) devices in the medical and industrial sectors has grown a lot. These situations with a lot of data have needs that go beyond what most cloud and edge computing frameworks can do when it comes to processing in real time, energy efficiency, and security. The inclusion of quantum computing, which can handle a lot of processing at once and execute more sophisticated tasks at once, could transform the way things are done in a big manner. When it comes to handling sensitive and different IoT, the current Edge-Cloud designs have a few big problems: they don't scale effectively, they have a lot of latency, and they do not have enough capacity. In order for predictive maintenance and automation to perform well in industrial Internet of Things (IIoT) contexts, they need to be able to address problems and act promptly. On the other hand, late diagnoses and data breaches are very dangerous for medical settings. All of these key factors; the computing burden, the reaction speed, and the security are handled by one unified design. The Quantum-Enhanced Edge-Cloud Computing Framework (QEECF) is proposed to speed up essential operations including encryption, finding anomalies, and making decisions in real time. With this technology, the quantum processing units (QPUs) are added to both the cloud servers and the edge nodes at the same time. The framework uses a hybrid quantum-classical scheduling strategy that combines quantum annealing with variational circuits to give the best possible job offloading. The objective of a federated quantum learning system is to keep sensitive data safe. This data is stored in databases all around the world, like those used in business and healthcare. We evaluate the technology by running simulations of things like smart manufacturing and keeping an eye on healthcare. These simulations are easier using IBM Qiskit and edge simulators. When compared to standard Edge-Cloud arrangements, QEECF might increase data throughput by 36%, cut job delays by 43%, and improve the accuracy of finding anomalies by 48%.
The rapid digitalization of manufacturing systems has led to the widespread adoption of Industrial Internet of Things (IIoT) networks, enabling real-time monitoring, data-driven optimization, and intelligent automation. A critical application within this paradigm is predictive maintenance, which aims to anticipate equipment failures before they occur, thereby reducing downtime, operational costs, and safety risks. However, traditional machine learning approaches for predictive maintenance and anomaly detection often struggle with high-dimensional sensor data, complex nonlinear relationships, and scalability constraints in large industrial environments. This paper explores the emerging role of Quantum Machine Learning (QML) as a novel computational framework for enhancing predictive maintenance and anomaly detection in smart manufacturing systems. By leveraging quantum principles such as superposition and quantum-enhanced feature spaces, QML algorithms offer new possibilities for processing complex industrial data more efficiently and accurately. The study examines hybrid quantum–classical models for fault prediction, early anomaly detection, and pattern recognition in IIoT networks, highlighting their potential advantages over classical methods. Challenges related to hardware limitations, data encoding, and industrial deployment are also discussed, providing a balanced perspective on the feasibility and future impact of quantum-enhanced intelligence in smart manufacturing.
The rapid expansion of the Internet of Things (IoT) has transformed the way physical devices communicate, process information, and provide intelligent services.With billions of interconnected devices generating enormous volumes of heterogeneous data, conventional computing architectures increasingly face limitations in computational efficiency, security, optimisation, and scalability.Quantum computing has emerged as a disruptive paradigm capable of solving computationally intensive problems exponentially faster than classical computers for selected problem classes.Integrating quantum computing with IoT offers unprecedented opportunities for intelligent decision-making, real-time optimisation, secure communication, and enhanced machine learning.Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems.Applications span healthcare, industrial automation, smart cities,
In the rapidly evolving landscape of Industry 4.0, industrial systems have become increasingly complex, interconnected, and data-intensive. Traditional computational methods often fall short in managing the highdimensional, nonlinear challenges presented by such environments. This paper explores a novel framework that integrates Big Data analytics with quantum computing techniques to model and optimize complex industrial systems-including Smart Grids, Supply Chains, and Manufacturing processes. By leveraging algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Quantum Machine Learning (QML), the study demonstrates substantial improvements in processing time, accuracy, and resource efficiency compared to classical methods. Empirical evaluation through a structured experimental design and statistical validation via ANOVA confirms that hybrid quantum-classical approaches outperform traditional models across multiple performance metrics. This integration paves the way for real-time, scalable, and intelligent industrial optimization, offering a transformative leap in operational decision-making, fault prediction, and strategic control.
The Industrial Internet of Things (IIoT) has transformed the concept of predictive maintenance by creating the possibility of real-time monitoring of machinery and equipment. However, the large volumes and complexity of data generated by IIoT sensors do pose significant challenges for data processing techniques. Quantum computing can be a transformative solution based on quantum algorithms that might improve predictive maintenance models. This paper discusses the integration of quantum algorithms on Google Quantum AI for optimising IIoT-based predictive maintenance systems. Techniques such as Quantum Support Vector Machines (QSVM) and quantum k-means clustering enable faster data processing and further enhance the accuracy of the anomaly detection process. Techniques such as Quantum Approximate Optimization Algorithm (QAOA) help in optimizing schedules for maintenance and resource allocation to reduce downtime and operational costs. Therefore, quantum simulations do better forecasts with the proper handling of equipment wear, and material degradation, bringing with themselves proper maintenance actions taken proactively. The real-time, parallel processing capability from the combined quantum computer and IIoT streams will lead to better, faster, low-cost maintenance decisions. Sustainably implemented quantum-fortified techniques are expected to give the least or no false positives, ensure events of unplanned downtime occurrence, and optimize maintenance resource usage. While further quantum advancement and breaking data integration challenges remain to be faced before reaching mass impact, now with the advancement of quantum technology it is possible to drastically change the landscape in the industrial space for predictive maintenance by revealing long-term efficiency gains in addition to cost savings. The paper unfolds potential advantages, challenges, and future direction on implementing quantum algorithms for IIoT-driven predictive maintenance systems.