Quantum Machine Learning for Multi-Robot-Assisted Tactical Augmented Reality
Bibliographic record
Abstract
Dismounted situational awareness (DSA) is a critical component of military operations. It is enhanced by tactical augmented reality (TAR) systems that overlay digital information onto soldiers' physical environments. Traditional TAR systems rely predominantly on data from soldier-mounted cameras, which can limit their effectiveness and increase the risk of soldiers being exposed to unseen threats. To address these challenges, we propose a new TAR framework called Tactical Augmented Reality on the Move (TAROTM). TAROTM utilizes advanced military robots, such as quadruped unmanned ground vehicles (QUGVs), that are organized into specialized collaborative teams to support sensing, data processing, storage, and analytics. Given the significant volume of data, amount of traffic, and delay constraints associated with TAROTM, we explore quantum machine learning (QML)'s potential to enable real-time data processing, analytics, and distribution. As a case study, we employ QML to optimize sensor-to-shooter data routing in TAROTM. Additionally, we discuss the challenges and opportunities associated with integrating QML in the TAROTM system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".