The Internet of Things (IoT) is central to the digital transformation of modern infrastructure, enabling intelligent environments across various sectors, including healthcare, industry, agriculture, and smart cities. However, as IoT networks continue to scale in size and complexity, they face several critical challenges. These include the need for real-time processing of high-dimensional, heterogeneous sensor data; maintaining energy efficiency in resource-constrained edge devices; ensuring robust performance in the presence of noise, communication failures, and environmental disturbances; and safeguarding data integrity against increasingly sophisticated cyber threats. Traditional machine learning models often fall short in addressing these demands due to inherent limitations in scalability, adaptability, and resilience under uncertain or adversarial conditions.

This project proposes the integration of quantum machine learning (QML) and hybrid quantum-classical approaches to address these limitations. It explores the use of quantum neural networks for real-time anomaly detection in sensor data streams, variational quantum circuits for energy-aware device coordination and task scheduling, and quantum-inspired reinforcement learning for adaptive resource management in decentralized Internet of Things (IoT) systems. To enhance data diversity and model training, quantum generative models will support simulation and augmentation, while federated learning frameworks incorporating quantum machine learning (QML) will preserve data privacy across distributed devices. Furthermore, the project evaluates the robustness of QML models under practical quantum noise scenarios, ensuring their reliability on current Noisy Intermediate-Scale Quantum (NISQ) hardware.

Together, these quantum-enhanced techniques aim to significantly improve the scalability, adaptability, and trustworthiness of next-generation IoT infrastructures, delivering greater energy efficiency, resilience, and built-in security.