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

TL;DR

This paper introduces quantum-cognitive tunnelling neural networks that improve classification and sentiment analysis in military contexts, aiming to enhance AI reasoning in battlefield scenarios.

Contribution

It presents novel QT-based neural networks tailored for military image classification and sentiment analysis, integrating quantum cognition principles into AI models.

Findings

QT-based models outperform traditional methods in vehicle classification.

Enhanced sentiment detection accuracy in military-specific contexts.

Potential for improved AI reasoning in battlefield applications.

Abstract

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