Experimental Assessment of Quantum PWM Performance in DC Motor Speed Control Applications
Bibliographic record
Abstract
Power electronics has played a critical role in improving motor drive operation during the last few decades. More precisely, the pulse-width modulation (PWM) and its derivatives have piqued the scientific community's interest due to their straightforward implementation. However, as precision and power quality requirements increased, techniques and approaches became more time-consuming and complex. The introduction of quantum computing proved to be a potential answer to many complicated issues that traditional computers are unable to tackle. The intrinsic quantum features of superposition, entanglement, and interference provide several possibilities to implement quantum and quantum-inspired solutions, which are transforming multiple engineering domains. This paper provides experimental validation of the quantum version of PWM, namely the QPWM. The method includes an inventive real number comparator. The experimental results show that the quantum technique surpasses its conventional counterpart in terms of precision and speedy convergence, but at the cost of slightly higher switching, which represents a step forward from the hardware- based implementation of quantum-inspired solutions, which have the potential to change a broad spectrum of technical and industrial domains.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".