Figure 1:
Quantum tunnelling enhances machine learning by incorporating models of human-like bistable perception of optical illusions and cognitive biases into the neural network. The principle of energy quantisation aligns with human mental states (depicted by lines on the head silhouettes), where transitions between energy levels enable nuanced militarycivilian vehicle differentiation.
Figure 2:
Example of a CIFAR-military vehicle subdataset
Table 1:
Positive and Negative Words: Military Context
| Positive Words | Negative Words | ||
|---|---|---|---|
| Achieve | Advance | Abort | Ambiguous |
| Authorize | Clear | Breakdown | Cancel |
| Command | Confirm | Compromised | Conflicted |
| Decisive | Definitive | Degrade | Defeat |
| Deploy | Designated | Denied | Disrupt |
| Effective | Engage | Doubtful | Failure |
| Established | Mission-ready | Ineffective | Misfire |
| Objective-secured | On-target | Obstructed | Off-course |
| Success | Validated | Unconfirmed | Void |
Figure 3:
Accuracy over epochs: (a) QT-RNN sentiment analysis, (b) QT-BNN military-civilian classification.Accuracy of the respective classical models shown for reference.
Figure 4:
Representative examples of civilian vehicles misclassified as military by the QT-BNN and classical models.