Purity benchmarking study of error coherence in a superconducting qubit
Bibliographic record
Abstract
In this study, we employ purity benchmarking (PB) to explore the dynamics of gate noise in a superconducting qubit system. Over 1110 h of observations on an Xmon qubit, we simultaneously measure the coherence noise budget across two different operational frequencies. We find that incoherent errors, which predominate in overall error rates, exhibit minimal frequency dependence, suggesting that they are primarily due to wideband, diffusive incoherent error sources. In contrast, coherent errors, although less prevalent, show significant sensitivity to operational frequency variations and telegraphic noise. We hypothesize that this sensitivity is due to interactions with a single strongly coupled environmental defect—modeled as a two-level system—which influences qubit control parameters and causes coherent calibration errors. Our results also demonstrate that PB offers improved sensitivity, capturing additional dynamics that conventional relaxation time measurements cannot detect, thus presenting a more comprehensive method for capturing dynamic interactions within quantum systems. The intricate nature of these coherence dynamics underscores the need for further research.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".