Extending comb-based spectral estimation to multiaxis quantum noise
Bibliographic record
Abstract
We show how to achieve full spectral characterization of general multiaxis additive noise on a single qubit, including arbitrary cross-axis noise correlations. Our pulsed spectral estimation technique is based on sequence repetition and frequency-comb sampling and is applicable in principle even to models where a large qubit energy splitting is present, as long as the noise is stationary and a second-order (Gaussian) approximation to the controlled reduced dynamics is viable. A key innovation in our approach is a spherical representation of the noise in terms of operators that couple directly to raising and lowering qubit operators, which is instrumental to show that only three suitably defined spectra effectively contribute in the large-splitting regime. Our result is crucial to extend the applicability of comb-based spectral estimation, which has been so far employed under the assumption of dephasing-dominated dynamics, to experimental platforms where both ${T}_{1}$ and ${T}_{2}$ processes may occur on comparable timescales or be otherwise significant, such as superconducting qubits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.012 |
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 teacher head, 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".