QUANTUM DISCORD AS A RESOURCE IN QUANTUM COMMUNICATION
Bibliographic record
Abstract
As quantum technologies move from the issues of principle to those of practice, it is important to understand the limitations on attaining tangible quantum advantages. In the realm of quantum communication, quantum discord captures the damaging effects of a decoherent environment. This is a consequence of quantum discord quantifying the advantage of quantum coherence in quantum communication. This establishes quantum discord as a resource for quantum communication processes. We discuss this progress, which derives a quantitative relation between the yield of the fully quantum Slepian–Wolf (FQSW) protocol in the presence of noise and the quantum discord of the state involved. The significance of quantum discord in noisy versions of teleportation, super-dense coding, entanglement distillation and quantum state merging are discussed. These results lead to open questions regarding the tradeoff between quantum entanglement and discord in choosing the optimal quantum states for attaining palpable quantum advantages in noisy quantum protocols.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".