Link Configuration for Fidelity-Constrained Entanglement Routing in Quantum Networks
Bibliographic record
Abstract
Entanglement routing (ER) in quantum networks must guarantee entanglement fidelity, a property that is crucial for applications such as quantum key distribution, quantum computation, and quantum sensing. Conventional ER approaches assume that network links can only generate entanglements with a fixed fidelity, and then they rely on purification to improve end-to-end fidelities. However, recent advances in entanglement generation technologies show that quantum links can be configured by choosing among different fidelity/entanglement-rate combinations (defined in this paper as link configurations), hence enabling a more flexible assignment of quantum-network resources for meeting specific application requirements. To exploit this opportunity, we introduce the problem of link configuration for fidelity-constrained routing and purification (LC-FCRP) in Quantum Networks. We first formulate a simplified FCRP version as a Mixed Integer Linear Programming (MILP) model, where the link fidelity can be adjusted within a finite set. Then, to explore the full space of possible link configurations, we propose a link configuration algorithm based on a novel shortest-path-based fidelity determination (SPFD) algorithm w/o Bayesian Optimization, which can be applied on top of any existing ER algorithm. Numerical results demonstrate that link configuration improves the acceptance ratio of existing ER algorithms by 87%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".