Reprogrammable and high-precision holographic optical addressing of trapped ions for scalable quantum control
Bibliographic record
Abstract
Abstract High-precision, individually programmable manipulation of quantum particles is crucial for scaling up quantum information processing (QIP) systems such as laser-cooled trapped-ions. However, restricting undesirable “crosstalk” in optical manipulation of ion qubits is fundamentally challenging due to micron-level inter-ion separation. Further, inhomogeneous ion spacing and high susceptibility to aberrations at UV wavelengths suitable for most ion-species pose severe challenges. Here, we demonstrate high-precision individual addressing ( λ = 369.5 nm) of Yb + using a reprogrammable Fourier hologram. The precision is achieved through in-situ aberration characterization via the trapped ion, and compensating (to λ /20) with the hologram. Using an iterative Fourier transformation algorithm (IFTA), we demonstrate an ultra-low (<10 −4 ) intensity crosstalk error in creating arbitrary pair-wise addressing profiles, suitable for over fifty ions. This scheme relies on standard commercial hardware, can be readily extended to over a hundred ions, and adapted to other ion-species and quantum platforms.
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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 | 0.001 |
| 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".