Optical Design for an Open Access Trapped Ion Quantum Processor
Bibliographic record
Abstract
The future of quantum information is scaling to a ever larger number of qubits. Doing so will require advances in the approach for all aspects of current experimental systems. The QuantumION project is a large step in this direction. This project will provide an open-access trapped ion quantum processor for the research community. The necessary innovations for how QuantumION will be controlled and assembled represents a paradigm shift from ad-hock disparate systems running the experiment to a well engineered, integrated, quantum platform. These innovations will require coordinated work from many contributors, and this thesis covers a few specific aspects of QuantumION to which the author has contributed. \n \nIn this thesis laser cooling is numerically investigated with special attention paid to external heating rates, laser linewidths, power limitations, and laser direction. Considering all these imperfections a set of laser parameters are presented for both quenched resolved sideband cooling and electromagnetically induced transparency cooling that in concert will cool all motional modes to the ground state. A novel individual addressing (IA) scheme is presented with detailed simulation showing 10⁻⁵ intensity cross talk and the first attempt at realizing this IA scheme is presented. The design philosophy for opto-mechanical assemblies in QuantumION is discussed and an example assembly is walked through the design process.
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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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".