Isotope-selective laser ablation ion-trap loading of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi>Ba</mml:mi><mml:none/><mml:mo>+</mml:mo><mml:mprescripts/><mml:none/><mml:mn>137</mml:mn></mml:mmultiscripts></mml:math> using a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>BaCl</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> target
Bibliographic record
Abstract
The $^{133}\mathrm{Ba}^{+}$ ion is a promising candidate as a high-fidelity qubit, and the $^{137}\mathrm{Ba}^{+}$ isotope is promising as a high-fidelity qudit $(d>2)$. Barium metal is very reactive, and $^{133}\mathrm{Ba}^{+}$ is radioactive and can only be sourced in small quantities, so the most commonly used loading method, oven heating, is less suited for barium and is currently not possible for $^{133}\mathrm{Ba}^{+}$. Pulsed laser ablation solves both of these problems by utilizing compound barium sources while also giving some distinct advantages, such as fast loading, less displaced material, and lower heat load near the ion trap. Because of the relatively low abundances of the isotopes of interest, a two-step photoionization technique is used, which gives us the ability to selectively load isotopes. Characterization of the ablation process for our ${\mathrm{BaCl}}_{2}$ targets are presented, including observation of neutral and ion ablation-fluence regimes, preparation and conditioning, lifetimes of ablation spots, and plume velocity distributions. We show that by using laser ablation on ${\mathrm{BaCl}}_{2}$ salt targets with a two-step photoionization method, we can produce and trap barium ions reliably. Furthermore, we demonstrate that with our photoionization method, we can trap $^{137}\mathrm{Ba}^{+}$ with an enhanced selectivity compared to its natural abundance.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".