Discerning quantum memories based on electromagnetically-induced-transparency and Autler-Townes-splitting protocols
Bibliographic record
Abstract
Electromagnetically induced transparency (EIT) and Autler-Townes splitting (ATS) are similar, but different quantum optical phenomena: EIT results from a Fano interference, whereas ATS is described by the ac Stark effect. Likewise, despite their close resemblance, light-storage techniques based on the EIT memory protocol and the recently proposed ATS memory protocol [Saglamyurek et al., Nat. Photon. 12, 774 (2018)] are distinct: The EIT protocol relies on adiabatic elimination of absorption, whereas the ATS protocol is based on absorption. In this article, we elaborate on the distinction between EIT and ATS memory protocols through numerical analysis and experimental demonstrations in a cold rubidium ensemble. We find that their storage characteristics manifest opposite limits of the light-matter interaction due to their inherent adiabatic versus nonadiabatic nature. Furthermore, we determine optimal memory conditions for each protocol and analyze ambiguous regimes in the case of broadband storage, where nonoptimal memory implementations can possess characteristics of both EIT and ATS protocols. We anticipate that this investigation will lead to deeper understanding and improved technical development of quantum memories, while clarifying distinctions between the EIT and ATS protocols.
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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 | 0.002 |
| 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 | 0.002 |
| 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".