A quantum direct torque control method for permanent magnet synchronous machines
Bibliographic record
Abstract
This study compares classical direct torque control (DTC) methods with a proposed quantum direct torque control (QDTC) strategy for synchronous machines. A quantum comparator is developed by implementing a quantum subtractor between real numbers ranging from -100 % to +100 %, and a quantum sign function is developed using this digital quantum subtractor. The QDTC implementation involved the use of quantum versions of the classic logical AND and OR gates. Simulation results indicate that the QDTC method significantly reduces torque ripple, with a ripple torque factor of 0.0392 compared to 0.0417 for the classical DTC. The QDTC approach also required 5.2 % fewer commutations (9.81 × 10 4 ) compared to the classical approach (1.035 × 10 5 ), which increases the longevity of the power components. Finally, the total harmonic distortion (THD) was lower for the QDTC method compared to the classical strategy. The results indicate that the proposed QDTC method either matches or surpasses the performance of the classical method across several metrics. Specifically, the reduced torque ripple and commutation frequency leads to smoother motor operation and longer component lifespans, while lower THD is indicative of greater motor efficiency.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".