Federated Quantum Neural Network with Quantum Teleportation for Resource Optimization in Future Wireless Communication
Bibliographic record
Abstract
The following study introduces FT-QNN, a federated and quantum teleportation –based quantum neural network, utilized to optimize resource allocation for future wireless communications. The proposed FT-QNN consists of edge quantum neural networks (QNNs) and a cloud QNN, while quantum teleportation allows the cloud QNN to obtain the outputs of edge QNNs without requiring prior measurements on the output states, allowing the cloud to process the outputs directly as quantum states. As a particular case to demonstrate its applicability for wireless resource allocation, FT-QNN is then employed to optimize transmit power allocation coefficients in a power domain non-orthogonal multiple access (NOMA)-based system, aiming to maximize the achievable sum-rate. FT-QNN yields lower complexity compared to a distributed QNN scheme without quantum teleportation, while the numerical results also demonstrated that the FT-QNN is capable to achieve a similar sum-rate compared to the scheme without quantum teleportation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".