Influence of random telegraph noise on quantum bit gate operation
Bibliographic record
Abstract
We consider the problem of analyzing spin-flip qubit gate operation in the presence of Random Telegraph Noise (RTN). Our compressive approach is the following. By using the Feynman disentangling operators method, we calculate the spin-flip probability of qubit driven by different kinds of composite pulses, e.g., Constant pulse (C-pulse), Quantum Well pulse (QW-pulse), and Barrier Potential pulse (BP-pulse) in the presence of RTN. When composite pulses and RTN act in the x-direction and z-direction respectively, we calculate the optimal time to achieve perfect spin-flip probability of qubit. We report that the highest fidelity of spin-flip qubit can be achieved by using C-pulse, followed by BP-pulse and QW-pulse. For a more general case, we have tested several pulse sequences for achieving high fidelity quantum gates, where we use the pulses acting in different directions. From the calculations, we find that high fidelity of qubit gate operation in the presence of RTN is achieved when QW-pulse, BP-pulse, and C-pulse act in the x-direction, y-direction, and z-direction, respectively. We extend our investigations for multiple QW and BP pulses while choosing the C-pulse amplitude constant in the presence of RTN. The results of calculation show that 98.5% fidelity can be achieved throughout the course of RTN that may be beneficial for quantum error correction.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".