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The Internet of Things (IoT) is a global network of millions of devices connected in wireless that exchange data. Multiple data are aiming to be observed through a single platform, and it becomes necessary to evaluate accuracy in order to realize the ideal IoT platform. IoT data collection is more important to meet the growing data analysis in demanding time-sensitive and real-time decision making. There has been a lot of effort involved in developing efficient quantum neural networks due to the success over the traditional neural network. This research proposes a new deep residual learning based quantum–classical neural network (Res-QCNN). The residual structural block is connected to the quantum neural network to examine and provide a training algorithm for analysing the IoT platform. Simultaneously, the benefits and drawbacks of translating quantum idea form deep residual learning are discussed. As a result, training algorithm is performing in the proposed model from beginning to end, similar working of backpropagation in traditional neural networks. When compared to existing, the Res-QCNN completes better when learning a Unitary function and has robustness for noisy data.
El-Aziz et al. (Thu,) studied this question.