Microgram BaCl2 ablation targets for trapped ion experiments
Bibliographic record
Abstract
Trapped ions for quantum information processing have been an area of intense study due to the extraordinarily high fidelity operations that have been reported experimentally. Specifically, barium trapped ions have been shown to have exceptional state-preparation and measurement fidelities. The 133Ba+ (I = 1/2) isotope in particular is a promising candidate for large-scale quantum computing experiments. However, a major pitfall with this isotope is that it is radioactive and is thus generally used in microgram quantities to satisfy safety regulations. We describe a new method for creating microgram barium chloride (BaCl2) ablation targets for use in trapped ion experiments and compare our procedure to previous methods. We outline two recipes for the fabrication of ablation targets that increase the production of neutral atoms for isotope-selective loading of barium ions. We show that heat-treatment of the ablation targets greatly increases the consistency at which neutral atoms can be produced, and we characterize the uniformity of these targets using trap-independent techniques such as energy dispersive x-ray spectroscopy and neutral fluorescence collection. Our comparison between fabrication techniques and the demonstration of consistent neutral fluorescence paves a path toward reliable loading of 133Ba+ in surface traps and opens opportunities for scalable quantum computing with this isotope.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.002 |
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".