An Optimized Equivariant Quantum Network for Ingenious Building Monitoring and Control using an Android Application

For enhancing the security and functionality of smart environments, motion recognition, classification and intruder detection has to be added into the building monitoring and control system. This research developed a Self-Guided Efficient Net UNet based Equivariant Quantum Network with Alpine Skiing Optimization (SGENUN-EQN-ASO) for improving intruder detection in buildings by utilizing an Android application for real-time motion detection and categorization. To prepare the data for analysis, processing can be done on the Self-Guided Filtering (SGF). An enhanced approach is Essential Objectness, Cascade Networks, Equivariant Quantum Convolutional Neural Networks (EquivQCNN) to enhance the detection precision and EfficientNet-based EE-UNet for precise motion assessment. Thus, the Android application works as monitoring and control interface providing real-time notifications and the answers to detected intrusions. With the use of optimisation techniques like Alpine Skiing Optimisation (ASO), which is used to fine-tune the hyperparameters of Equivariant Quantum Neural Networks (EQNNs), the system is able to provide stable and flexible performance. The results demonstrating that the integration of the latest machine learning approaches into the solutions in smart buildings brings significant enhancement to the detection effectiveness and response times, as to the overall security and management.