Optimizing Link-Level Entanglement Generation in Quantum Networks with Unequal Link Lengths
Bibliographic record
Abstract
Quantum internet offers a variety of applications that either enhance or surpass its classical counterpart. Quantum repeaters are imperative to a quantum network as they connect between two quantum nodes, and create quantum entanglement between two targeted nodes with entanglement swapping protocol which require resource from both connections. As a quantum state has a finite decoherent time, it is crucial that a connection protocol is performed efficiently and within suitable time interval, so that two end-to-end matter qubits are entangled. We simulate a second generation network of quantum repeaters, one that is capable of quantum error correction, to estimate the waiting time needed to establish connection between distant nodes. Simulations are performed for a linear chain of nodes with unequal path lengths, and loss rates. We find that asymmetry of path lengths contributes to the waiting time of physical qubits. By simply adjusting the photon generation rate from one of its nodes, the waiting time is reduced while the time to complete quantum state tomography remains unaffected. This simple adjustment can play important roles in compensating for distance asymmetry when a quantum repeater is not stationary.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".