Tailoring the ultrafast control of quantum dot excitons using optical pulse shaping
Bibliographic record
Abstract
Abstract Femtosecond pulse shaping provides a means to tailor the interaction of light with matter, enabling the optimization of optical control processes and the achievement of a target final quantum state of the matter system. While this approach has found widespread application in the control of atomic and molecular systems, its use for solid state quantum systems remains in its infancy. This review covers our application of this approach to the manipulation of the quantum states of excitons in semiconductor quantum dots (QDs). The achievement of simultaneous π, 2π rotations on excitons in two different QDs using a single engineered infrared pulse illustrates the flexibility of the pulse shaping approach for solid state quantum systems. This versatility is further explored through simulations that show the feasibility of arbitrary SU(2) control of several quantum dots. Shaping of femtosecond infrared control pulses enables the demonstration of adiabatic rapid passage in a single QD on a subpicosecond time scale, representing a substantial speedup and an important step towards the realization of dynamical decoupling. The dependence of the exciton inversion efficiency on the sign of pulse chirp confirms the role of (and ability to control) phonon‐related dephasing. (© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.000 | 0.000 |
| 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".