Efficient Pulse‐Excitation Techniques for Single Photon Sources from Quantum Dots in Optical Cavities
Bibliographic record
Abstract
Abstract Rigorous and intuitive master equation models are presented to study on‐demand single photon sources from pulse‐excited quantum dots coupled to optical cavities. Three methods of source excitation are considered: resonant pi‐pulse, off‐resonant phonon‐assisted inversion, and two‐photon excitation of a biexciton–exciton cascade, and the effect of the pulse excitation process on the quantum indistinguishability, efficiency, and purity of emitted photons is investigated. By explicitly modelling the time‐dependent pulsed excitation process in a manner which captures non‐Markovian effects associated with coupling to photon and phonon reservoirs, it is found that photons of near‐unity indistinguishability can be emitted with over 90% efficiency for all these schemes, with the off‐resonant schemes not necessarily requiring polarization filtering due to the frequency separation of the excitation pulse, and allowing for very high single photon purities. Furthermore, the off‐resonant methods are shown to be robust over certain parameter regimes, with less stringent requirements on the excitation pulse duration in particular. Also, a semi‐analytical simplification of the master equation is derived for the off‐resonant drive, which gives insight into the important role that exciton–phonon decoupling for a strong drive plays in the off‐resonant phonon‐assisted inversion process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".