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

arXiv: https://arxiv.org/abs/2507.18645 · HTML: https://mlsanity.com/paper/2507.18645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 13 of 13)

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- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [A Planck Radiation and Quantization Scheme for Human Cognition and Language](https://mlsanity.com/paper/2201.03306/) (2022)
- [Quantum-Inspired Neural Network Model of Optical Illusions](https://mlsanity.com/paper/2312.03447/) (2023)
- [Human Perception as a Phenomenon of Quantization](https://mlsanity.com/paper/2208.03726/) (2022)
- [Quantum-tunnelling deep neural network for optical illusion recognition](https://mlsanity.com/paper/2407.11013/) (2024)
- [The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics](https://mlsanity.com/paper/2310.00267/) (2023)
- [Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations](https://mlsanity.com/paper/2412.08010/) (2024)
- [Exploring Cognitive Paradoxes in Video Games: A Quantum Mechanical Perspective](https://mlsanity.com/paper/2307.08758/) (2023)
- [Fake news: No ban, No spread -- with Sequestration](https://mlsanity.com/paper/2401.02167/) (2024)
- [Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks](https://mlsanity.com/paper/2501.02671/) (2025)

## Abstract (arXiv)

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.