Quantum Deep Deterministic Policy Gradient for Digital Twin-Enabled Semantic IoV Networks
Why this work is in the frame
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Bibliographic record
Abstract
Internet of Vehicles (IoV) networks are becoming more complex as they require real-time decision-making and efficient resource management. These demands make it difficult to maintain stable and reliable operations. The challenges are especially severe in dynamic and time-varying environments. To address these limitations, we propose a framework that integrates the quantum-based deep deterministic policy gradient (Q-DDPG) with digital twin networks (DTN) for distributed semantic optimization in dynamic IoV environments. The framework leverages quantum computing, such as superposition and entanglement, to enhance distributed semantic decisions. DTNs provide real-time modeling for efficient task offloading and adaptive resource allocation in decentralized IoV environments under varying conditions and uncertainties. The numerical results validate the robustness of the proposed approach, significantly reduce latency, and improve energy efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it