Quantum-Enhanced Edge-Cloud Framework for Scalable Medical and Industrial IoT Systems

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%.