Memory and Transduction Prospects for Silicon <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>T</mml:mi></mml:math> Center Devices
Bibliographic record
Abstract
The T center, a silicon-native spin-photon interface with telecommunication-band optical transitions and long-lived microwave qubits, offers an appealing new platform for both optical quantum memory and microwave-to-optical telecommunication-band transduction.A wide range of quantum memory and transduction schemes could be implemented withT center ensembles with sufficient optical depth, with advantages and disadvantages that depend sensitively on the ensemble properties.In this work we characterize T center spin ensembles to inform device design.We perform the first T ensemble optical depth measurement and calculate the improvement in center density or resonant optical enhancement required for efficient optical quantum memory.We further demonstrate a coherent microwave interface by coherent population trapping and Autler-Townes splitting.We then determine the most promising microwave and optical quantum memory protocol for such ensembles.By estimating the memory efficiency both in free space and in the presence of a cavity, we show that efficient optical memory is possible with reasonable optical density forecasts.Finally, we formulate a transduction proposal and discuss the achievable efficiency and fidelity.
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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 | 0.001 | 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".