Spin Lifetime Mechanisms in Erbium-Doped Fiber for Quantum Network Applications
Bibliographic record
Abstract
Erbium-doped silica fiber (EDF) is evaluated as a telecom-band quantum-memory platform via spectral hole burning at millikelvin temperatures. We map spin-population decay versus temperature (about 7 to 2400 mK) and magnetic field (0 to 200 mT), revealing two relaxation components above about 80 mK and, at about 7 mK, a third, ultra-slow component enabling more than 9 h population storage under optimized field. The relative component weights are nearly condition-independent, consistent with distinct ion classes. A single phenomenological model, combining Er-Er flip-flops, a TLS-mediated direct process with field and temperature scaling, and a Raman-like term, fits all data using shared exponents while allowing mechanism weights to vary between classes. We also observe a Lorentzian-to-Gaussian hole-shape crossover below about 80 mK, pointing to changing broadening mechanisms. These results establish long-lived spin-population storage in standard EDF and delineate a practical operating window. They motivate denser measurements across the below-80 mK crossover, isotope-resolved tests, and complementary optical-coherence studies.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".