Hybrid Quantum and Classical Machine Learning Classification for IoT-Based Air Quality Monitoring

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.