Quantum Connected Collaborative Learning with Superdense Coding for Wireless Internet-of-Everything Networks
Bibliographic record
Abstract
In recent years, distributed wireless communication optimization, where training data is stored remotely from local multi-access edge computing (MEC) processors to preserve data security privacy and minimize complexity, has seen noteworthy progress for relevant wireless Internet-of-Everything (WIoE) networks beyond 6G. Nonetheless, the exploding number of WIoE clients requires secure data storage and scaled data processing at the network and transmitter, which local processors might be unable to afford. Parallel to this, we are witnessing widespread quantum-enabled learning adoptions for optimizing wireless communications. The rapid growth of quantum technologies has introduced security concerns for classical channels, due to their potential to undermine classical cryptographic approaches. This paper, therefore, considers the adoption of quantum-enabled learning with quantum communication protocol, especially quantum secure direct communication (QSDC) via superdense coding. While the processing learning happens across different locations for next-generation WIoE networks, the QSDC prevents vulnerabilities of data poisoning and model stealing in connected quantum collaborative learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".