Decoherence and dephasing errors caused by the dc Stark effect in rapid ion transport
Bibliographic record
Abstract
We investigate the error due to the dc Stark effect for quantum information processing for trapped ion quantum computers using the scalable architecture proposed in D. Wineland et al. [J. Res. Natl. Inst. Stand. Technol. 103, 259 (1998)] and D. Kielpinski et al. [Nature (London) 417, 709 (2002)]. As the operational speed increases, dephasing and decoherence due to the dc Stark effect become prominent as a large electric field is applied for rapidly transporting ions. We estimate the relative significance of the decoherence and dephasing effects and find that the latter is dominant. We find that the minimum possible dephasing is quadratic in the length of a trap and an inverse cubic in the operational time scale. From these relations, we obtain the operational speed range at which the shifts, caused by the dc Stark effect, are no longer negligible, no matter on which trajectory the ion is transported. Without phase correction, the shortest time a qubit can be transferred across a 100-micrometer-long trap, without excessive error, is about 10 ns for a ${}^{40}{\mathrm{Ca}}^{+}$ ion and 50 ps for a ${}^{9}{\mathrm{Be}}^{+}$ ion. In practice, the accumulated error is difficult to track and to calculate; thus, our paper gives an estimate for the range of the speed limit imposed by the dc Stark effect.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".