Faking and Discriminating the Navigation Data of a Micro Aerial Vehicle\n Using Quantum Generative Adversarial Networks
Bibliographic record
Abstract
We show that the Quantum Generative Adversarial Network (QGAN) paradigm can\nbe employed by an adversary to learn generating data that deceives the\nmonitoring of a Cyber-Physical System (CPS) and to perpetrate a covert attack.\nAs a test case, the ideas are elaborated considering the navigation data of a\nMicro Aerial Vehicle (MAV). A concrete QGAN design is proposed to generate fake\nMAV navigation data. Initially, the adversary is entirely ignorant about the\ndynamics of the CPS, the strength of the approach from the point of view of the\nbad guy. A design is also proposed to discriminate between genuine and fake MAV\nnavigation data. The designs combine classical optimization, qubit quantum\ncomputing and photonic quantum computing. Using the PennyLane software\nsimulation, they are evaluated over a classical computing platform. We assess\nthe learning time and accuracy of the navigation data generator and\ndiscriminator versus space complexity, i.e., the amount of quantum memory\nneeded to solve the problem.\n
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".