Quantum Teleportation-based Control for Mobile Robots
Bibliographic record
Abstract
Quantum computing has garnered significant interest in robotics applications over the past few years. Demonstrating its superiority over traditional methods, it has shown many advantages in precision, rapid computation, and robustness for Manipulators and cell robots. In this paper, a remote control-based quantum computation tool is presented to ensure the trajectory tracking of a car-like mobile robot. In the proposed architecture, remote communication between the robot and the controller is achieved using quantum teleportation circuits. The controller is a classic PID controller, with its parameters optimized using a quantum-inspired particle swarm optimization (PSO) algorithm. The control scheme's performance is estimated using the root mean square error (RMSE), calculated from the tracking error, which is the difference between the reference position and the actual position of the robot. Simulation results show that the quantum-inspired PSO-based optimization of the PID controller yielded better performance in terms of prompt convergence and stability. Quantum teleportation also provided high accuracy when transferring control signals and measurement data. This work demonstrates that it is feasible to use quantum computing tools to enhance the real-time performance of long-distance, remotely controlled robotic systems, and that such telecontrol is inherently possible even at great distances or in hostile environments.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".