Using Quantum Fuzzy Inference Engines in Smart Cities

This paper addresses the escalating complexity of smart city environments by proposing the use of Quantum Fuzzy Inference Engine (QFIE) for enhanced control. Smart cities play a pivotal role in optimizing resource utilization and improving overall urban living. However, their intricate and interconnected nature demands advanced control algorithms. QFIE emerges as a promising solution due to its computational power and capability to handle uncertainty. In this research, the suitability of QFIE for the control of smart city environments is assessed for the very first time by the design and test of three different QFIE-based fuzzy rule base systems aiming to solve the problem of computing the average localization error in wireless sensor networks, the prediction of heating demands in buildings, and the control of traffic lights in a junction. In these scenarios, the experimental evaluation of QFIE shows improved control capability compared with classical algorithms.