Calculating Long Range Two Photon Visibility for Entanglement Swapping Based Quantum Communication via Sampling
Why this work is in the frame
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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-entanglement-swapping-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 entanglement-swapping 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.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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