The Concept of Quantum Teleportation for Remote Control of a Car-like Mobile Robot
Bibliographic record
Abstract
We describe a quantum teleportation protocol for exchanging data between a mobile robot and its control station. Because of the high cost of quantum network systems, we use MATLAB software to simulate the teleportation of data. Our simulation models the dynamic motion of a car-like mobile robot (CLMR), considering its mass and inertia and the environmental viscosity. Our remote control method accurately reproduces a mathematical model of the CLMR’s real-world motion. The CLMR’s trajectory is represented by differential equations, with the velocity calculated using the Jacobian matrix. The velocity inputs are teleported from the control station to the CLMR, enabling it to move. Nevertheless, physical constraints cause the deviation of the robot’s trajectory from the predicted trajectory. To correct this deviation, the CLMR’s current position is teleported to the control station. Before implementing this protocol, we calculate the quantum teleportation circuit, and we use quantum gates in matrix form to simulate the data teleportation process. The protocol’s accuracy is assessed by comparing the original data and teleported data, and a good match is obtained. This study demonstrates the feasibility of quantum teleportation for remotely controlling real-time robotic systems over long distances and in environments that interfere with classical wireless communication.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".