Direct Observation of Dissipation in Dynamical Search Algorithm using Transmon Qubits
Bibliographic record
Abstract
Abstract Following recent work [Fortschritte der Physik 66, 1700080 (2018)], the dissipation effect of the dynamical quantum search algorithm (DQSA) is investigated. Such an algorithm is realized with the interaction of multi superconducting transmon qubits inside a 3D bus cavity. The dissipation of such system is caused by managing the sensitivity to charge noise via tuning the qubit frequency by employing Josephson energy. The probabilities of marked and unmarked states for the present algorithm have been calculated analytically and numerically. Such probabilities of marked states are sensitive to any change in the dissipation parameter. A deficiency causes the dissipation for the marked states, and that deficiency is added to the unmarked states. It is interesting to mention that one of the datasets at large dissipation rates gives an observation of the marked states probabilities which is related to the decoherence free subspace. It is predicted that the algorithm can be successfully implemented in the current experiments.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".