AI-DRIVEN QUANTUM SENSING FOR HIGH-PRECISION ENVIRONMENTAL MONITORING

Abstract

On the integration of artificial intelligence with quantum sensing technologies for environmental monitoring, these make it much more precise, efficient and real time information end-user. In order to combat climate change markers, insulation effectiveness, and environmental hazard immediate response systems, this research dictates an AI based quantum sensing framework. Quantum augmented machine learning methods, i.e., Quantum Bayesian Networks and Quanvolutional Neural Networks, are used to improve accuracy of measurements from sensors and furthermore sensor calibration. In addition, the proposed system incorporates Q-SCALE for advanced sensor calibration to eliminate noise and improve sensor performance. The Quantum Wireless Sensing framework, in the form of quantum-based sensors, has an advantage in sensing dynamic environmental factors with an increased sensitivity. Some key findings include that AI quantum AI sensors are better than traditional sensors at alerting to abrupt environmental shifts, monitoring pollutants, testing greenhouse conditions, etc. Quantum entanglement was not used, and quantum sensors also achieved secure data transmission of distances beyond 50 kilometers. The proposed system achieves real-time data acquisition using lightweight models optimized for such resource constrained environments and reduces both the energy consumption. This major step in research will lead towards development of more accurate as well as scalable, sustainable environmental monitoring solutions with enhanced data security. As such, recent innovations such as AI coupled with nanomaterials and IoT-instigated quantum surveillance systems indicate the extent of this field’s capability.