A Purity Benchmarking Study of Superconducting Single-qubit Fluctuations
Bibliographic record
Abstract
Quantum processes are susceptible to errors. Over the years, numerous noise models, \nsuch as two-level system noise and flux noise, have been proposed by physicists to describe \nthe mechanisms behind the error sources affecting quantum processes. However, a compre- \nhensive understanding of the quantum noise landscape, particularly on longer timescales, \nis still under active exploration. This thesis contributes to this ongoing effort, exploring \nlong-term quantum noise through the lens of a superconducting Xmon transmon qubit. \nIn our study, we explore the long-term qubit noises by conducting continuous purity \nbenchmarking experiments, utilizing a set of established metrics to gauge the quantum \nerrors. These metrics, namely the average gate fidelity and unitarity, provides a more \ndetailed characterization of quantum error compared to the commonly studied variables \nsuch as T1 and frequency detuning, including characterization of the coherence property. \nThese metrics are also the subject of intense discussions, particularly in the fields of quan- \ntum algorithms and quantum information processing hardware development. We measured \nthe coherent and incoherent quantum error for very long time periods, up to 440 hours. \nThrough these experiments. we gain valuable insights into the nature of the quantum noise \nand its impact on qubit coherence. \nFollowing the experiments, we further attempt to reconcile our observations with well- \nestablished models, namely the two-level system and flux noise, through simultaneous \nmeasurements and comprehensive simulations. While we succeed in explaining certain \naspects of the experimental results, our findings also highlight intriguing discrepancies \nbetween experimental observations and simulations, thus prompting 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".