The rapidly emerging technology of Internet of Things (IoT) provides significant advancements in the domain of sensors and data collection in various environments, enabling improved decision making. Accuracy in data analysis, classification and fusion are raising as important challenges for the advancement of IoT. Machine Learning (ML) is expected to enhance the ability to recognize various patterns from IoT data and determine the best analytic process. These synergies are expected to have a vital role in IoT applications in industrial environment where safety and security are of utmost importance. In this work, a hybrid quantum ML classification scheme is proposed into an air quality IoT monitoring system in order to detect dangerous and abnormal situations (i.e. smoke detection and fire alarm). Furthermore, an evaluation of the proposed scheme is performed investigating the limits of classical and hybrid quantum ML approaches taking into account computational resources availability and efficiency tradeoff. In addition, it should be noted that feature reduction is also applied based on a Data Ensemble Refinement Greedy Algorithm (DERGA) optimizing the number of features employed. Finally, the noise impact over the behavior of hybrid quantum data classification is also examined in terms of accuracy under different varying parameters.
The rapidly emerging technology of Internet of Things (IoT) provides significant advancements in the domain of sensors and data collection in various environments, enabling improved decision making. Accuracy in data analysis, classification and fusion are raising as important challenges for the advancement of IoT. Machine Learning (ML) is expected to enhance the ability to recognize various patterns from IoT data and determine the best analytic process. These synergies are expected to have a vital role in IoT applications in industrial environment where safety and security are of utmost importance. In this work, a hybrid quantum ML classification scheme is proposed into an air quality IoT monitoring system in order to detect dangerous and abnormal situations (i.e. smoke detection and fire alarm). Furthermore, an evaluation of the proposed scheme is performed investigating the limits of classical and hybrid quantum ML approaches taking into account computational resources availability and efficiency tradeoff. In addition, it should be noted that feature reduction is also applied based on a Data Ensemble Refinement Greedy Algorithm (DERGA) optimizing the number of features employed. Finally, the noise impact over the behavior of hybrid quantum data classification is also examined in terms of accuracy under different varying parameters.
The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining quantum computing (QC) with machine learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. QC, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, QC, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating QC approaches with ML. It also addresses the challenges and limitations of integrating ML and QC in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.
The proliferation of large-scale Internet of Things (IoT) networks introduce significant challenges for real-time anomaly detection, particularly due to the massive volume, high speed, and heterogeneity of IoT-generated data. Traditional and deep learning-based models, while effective to some extent, often face computational bottlenecks that limit their scalability and responsiveness in complex IoT environments. To address these limitations, this paper investigates the application of Quantum Machine Learning (QML) for anomaly detection in IoT systems. By leveraging the parallelism and high-dimensional processing capabilities of quantum computing, QML offers a promising pathway to overcome current computational constraints. We propose a novel anomaly detection framework based on Quantum Generative Adversarial Networks (QGANs) designed to enhance security, reliability, and real-time responsiveness across diverse IoT scenarios, including smart homes, industrial IoT, healthcare monitoring, and critical infrastructure protection. Furthermore, this paper provides a comprehensive analysis of existing anomaly detection challenges, practical IoT applications, and outlines future research directions, including hybrid quantum-classical architectures, resource-efficient quantum algorithms for edge computing, and the integration of QML with emerging technologies to ensure scalable, secure, and robust IoT systems.
Industry 4.0 and other advancements are made possible by an Internet of Things (IoT), one of the emerging technologies. In addition, security becomes a difficult problem when IoT devices communicate over wireless channels. It has recently been feasible to detect intrusion in an IoT environment using machine learning techniques. The amount of computation needed for training grows dramatically with huge volume of data. As the amount of data increases, the running time of many machine learning algorithms increases drastically. At the intersection of traditional machine learning and quantum computing, quantum machine learning promises to revolutionize the processing and analysis of large data sets by utilizing the special benefits of quantum physics. This work introduces Hybrid Quantum Neural Network (HQNN) contrived for overcoming these challenges and facilitate efficient Quantum enabled machine learning for real time intrusion detection systems. Real network traces are used to validate our proposed intrusion detection technology and our approach shows remarkable improvement in accuracy against conventional approaches.
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 blistering development of the Internet of Things (IoT) has brought an urgent necessity to an efficient data processing at the end of the network, where devices are characterized by few computational capabilities, and little to no energy availability. Conventional machine learning (ML) solutions can offer high-performance on a centralized system, but do not provide the edge environments with the required latency and the corresponding performance. This paper gives a quantum machine learning (QML)-backed architecture that is used to optimize IoT edge processing, inspiring quantum feature encoding along with quantum neural networks with classical post-processing resource allocation. Different simulated IoT datasets were tested on the framework and they all represent applications to smart home, industrial IoT, and environment monitoring. According to experimental findings, processing latency (improved by 39 percent), energy consumption (improved by 40 percent), memory reorganization (improved by 40 percent), and prediction accuracy (improved by 6 per cent.) were significantly less than conventional ML models. The results demonstrate that QML models are able to make real-time decisions significant and scalable as well as sustainable in edge computing. Quantum-enhanced edge intelligence can significantly improve IoT data processing speed, efficiency, and smart decision-making.
Quantum Machine Learning (QML) improves quantum computational advantage by utilizing quantum mechanics to process and analyse information far faster than using classical techniques. One significant obstacle in the field of QML is the need for quantum hardware that is both fault-tolerant and noise resilient to implement and execute QML algorithms successfully. Overcoming the difficulties of QML can result in significant advancements in computational speed and accuracy. QML would enable the successful resolution of complex problems that are currently intractable using classical computing methods. This paper provides a comprehensive analysis of the limitations of classical Machine Learning (ML) and offers an overview of QML, highlighting its significance and foundational principles. We explore cutting-edge QML algorithms that leverage quantum parallelism and entanglement, specifically designed for Internet of Things (IoT) networks, to perform tasks more efficiently. We also discuss the architecture of a QML-based IoT network, focusing on integrating quantum computing capabilities into the vast network of interconnected IoT devices. Additionally, the paper introduces fresh perspectives on QML's potential applications, especially in IoT, and proposes future research directions. We offer insights into the structure of QML, its potential applications, and the architectural considerations for integrating QML into IoT. This paper is primarily targeted at engineering managers and strategic decision-makers seeking actionable guidance on the adoption of QML in industrial IoT contexts.