Quantum DRL for UAV-RIS-aided Maritime Communications with 6G Digital Twin Applications
Bibliographic record
Abstract
This paper presents a digital twin (DT) framework for the sixth-generation (6G) maritime communication, targeting extreme ultra-reliable low-latency communication (xURLLC). In particular, we propose a DT model, where the physical system integrates unmanned aerial vehicles with reconfigurable intelligent surfaces and a high-altitude platform featuring mobile edge computing. We formulate a nonlinear programming problem to minimize total xURLLC user latency while satisfying energy and computational constraints. Quantum proximal policy optimization (Q-PPO) employed at the DT layer is adopted to cope with dynamic network conditions. The simulation results demonstrate superior performance for Q-PPO, which converges rapidly with minimal state space and requires fewer iterations per episode compared to classical PPO. Hybrid-Q-PPO, incorporating a parameterized quantum circuit within the policy network, also delivers notable performance enhancements than classical PPO. Moreover, Q-PPO achieves higher overall rewards than Hybrid-Q-PPO. Both Q-PPO and Hybrid-Q-PPO significantly reduce latency and improve resource utilization, highlighting the effectiveness of QDRL.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".