Calculating Long Range Two Photon Visibility for Entanglement Swapping Based Quantum Communication via Sampling
Bibliographic record
Abstract
In this dissertation I develop a Monte-Carlo sampling approach to redress the enormous computational time required to calculate two-photon visibility for multiple-entanglementswapping-based long-distance quantum communication. I employ our theory to study both the realistic setting involving dark counts, multi-photon events and loss, and I also study the semi-idealistic case of perfect synchronized single-photon sources; this semi-idealistic case is used to verify my sampling method. My new sampling method enables successful, reliable calculation of visibility for up to six consecutive entanglementswapping stations. Although six entanglement-swapping stations lead to extremely low rates in the real-world setting, my sampling method for solving long-distance quantum communication rates and visibility serves as a valuable tool for modeling future viable quantum communication strategies incorporating promising technology such as optical quantum memory. past twenty-three years, and my dear friends, without whom my life can never be that wonderful. Finally, I want to thank her, who is the reason why I never give up on this project.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".