Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis

Abstract Prior work has demonstrated that incorporating well-known quantum-physical tunnelling probability into artificial neural network models effectively captures important nuances of human cognition and perception, particularly in the recognition of ambiguous objects and sentiment analysis. In this paper, we employ novel tunnelling-based artificial neural networks and assess their effectiveness in distinguishing customised images of military and civilian vehicles, as well as sentiment, using a proprietary military-specific vocabulary. We show that quantum tunnelling neural networks can enhance multimodal AI applications in battlefield scenarios, particularly within human-operated drone warfare contexts, imbuing AI with certain traits of human reasoning.