Unwanted couplings can induce amplification in quantum memories despite negligible apparent noise
Bibliographic record
Abstract
Theoretical quantum memory design often involves selectively focusing on certain energy levels to mimic an ideal Λ configuration, a common approach that may unintentionally overlook the impact of neighboring levels or undesired couplings. While this simplification may be justified in certain protocols or platforms, it can significantly distort the achievable memory performance. Through numerical semiclassical analysis, we show that the presence of unwanted energy levels and undesired couplings in an absorptive memory based on a nitrogen-vacancy center can significantly amplify the signal, resulting in memory efficiencies exceeding unity, a clear indication of unwanted noise at the quantum level. Strikingly, this effect occurs even when the apparent noise, i.e., output in the absence of an input field, is negligible. We then generalize our results using semianalytical estimations to analyze this amplification, and propose a strategy to reduce its effect. Our findings extend to memory platforms beyond nitrogen-vacancy centers; as an example, we also analyze a cavity-based rubidium memory that experiences the same issue.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".