A guided light system for agile individual addressing of Ba<sup>+</sup> qubits with 10<sup>−4</sup> level intensity crosstalk
Bibliographic record
Abstract
Abstract Trapped ions are one of the leading platforms for quantum information processing, exhibiting the highest gate and measurement fidelities of all contending hardware. In order to realize a universal quantum computer with trapped ions, independent and parallel control over the state of each qubit is necessary. The manipulation of individual qubit states in an ion chain via stimulated Raman transitions generally requires light focused on individual ions. In this manuscript, we present a novel, guided-light individual addressing system for hyperfine Ba+ qubits. The system takes advantage of laser-written waveguide technology, enabled by the atomic structure of Ba+, allowing the use of visible light to drive Raman transitions. Such waveguides define the spatial mode of light, suppressing aberrations that would have otherwise accumulated in a free-space optics set up. As a result, we demonstrate a nearest neighbor relative intensity crosstalk on the order of 10−4, without any active aberration compensation. This is comparable to or better than other previous demonstrations of individual addressing. At the same time, our modular approach provides independent and agile control over the amplitude, frequency, and phase of each channel; combining the strengths of previous implementations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".