Laser-Induced Spectral Diffusion and Excited-State Mixing of Silicon T Centers
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Bibliographic record
Abstract
To find practical application as photon sources for entangled optical-resource states or as spin-photon interfaces in entangled networks, semiconductor emitters must produce indistinguishable photons with high efficiency and spectral stability. Nanophotonic cavity integration increases efficiency and bandwidth but it also introduces environmental charge instability and spectral diffusion. Among various candidates, silicon color centers have emerged as compelling platforms for integrated-emitter quantum technologies. Here, we investigate the dynamics of spectral wandering in nanophotonics-coupled individual silicon T centers using spectral correlation measurements. We observe that spectral fluctuations are driven predominantly by the near-infrared excitation laser, consistent with a power-dependent Ornstein-Uhlenbeck process, and we show that the spectrum is stable for up to <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mn>1.5</a:mn> <a:mspace width="0.1em"/> <a:mi>ms</a:mi> </a:math> in the dark. We demonstrate a factor-of- <d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline"> <d:mn>35</d:mn> </d:math> narrowing of the emitter linewidth to <f:math xmlns:f="http://www.w3.org/1998/Math/MathML" display="inline"> <f:mn>110</f:mn> <f:mspace width="0.1em"/> <f:mi>MHz</f:mi> </f:math> using a resonance-check scheme and discuss the advantage for pairwise entanglement rates and optical-resource-state generators. Finally, we report laser-induced spin mixing in the excited state and discuss potential mechanisms common to both phenomena. These effects must be considered in calibrating T-center devices for high-performance entanglement generation.
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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 | 0.000 | 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 it