Generalized fast quasiadiabatic population transfer for improved qubit readout, shuttling, and noise mitigation
Bibliographic record
Abstract
Population-transfer schemes are commonly used to convert information robustly stored in some quantum system for manipulation and memory into more macroscopic degrees of freedom for measurement. These schemes may include, e.g., spin-to-charge conversion for spins in quantum dots, detuning of charge qubits between a noise-insensitive operating point and a measurement point, spatial shuttling of qubits encoded in spins or ions, and parity-to-charge conversion schemes for qubits based on Majorana zero modes. A common strategy is to use a slow (adiabatic) conversion. However, in an adiabatic scheme, the adiabaticity conditions on the one hand, and accumulation of errors through dephasing, leakage, and energy relaxation processes, on the other hand, limit the fidelity that can be achieved. Here, we give explicit fast quasiadiabatic (fast-QUAD) conversion strategies (pulse shapes) beyond the adiabatic approximation that allow for optimal state conversion. In contrast with many other approaches, here we account for noise in combination with pulse shaping. Although we restrict to noise sources that can be modeled by a classical fluctuating parameter, we allow generally for anisotropic non-Gaussian noise that is nevertheless sufficiently weak to lead to a small error. Inspired by analytic methods that have been developed for dynamical decoupling theory, we provide a general framework for unique noise mitigation strategies that can be tailored to the system and environment of interest.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".