Towards a Digital Twin of Medium-Voltage Circuit Breakers Using Quantum Algorithms and Deep Learning
Bibliographic record
Abstract
This paper proposes an approach that takes advantage of quantum computing combined with deep learning methods to design the digital twin of a medium-voltage circuit breaker. The method put forward in this study optimizes the search for an alternative to Sulfur hexafluoride, SF6 gas insulation in the fight against global warming, by replacing the use of SF<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> with a gas that is less harmful to the environment. The implementation of the QAOA quantum algorithm maximizes performance in calculating the dielectric strength of a gas mixture, while the integration of the deep learning method identifies the pattern of gas behavior in operation and predicts failures. The aim of creating the digital twin of a medium-voltage circuit breaker is to provide the electrical industry with a platform for virtual experimental testing with increased precision, saving time and resources - an important innovation in the world of electrical engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".