Quantum Connected Collaborative Learning with Superdense Coding for Wireless Internet-of-Everything Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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