Optimisation Applications of Quantum Computing in the Energy Business
Bibliographic record
Abstract
Abstract Quantum computing and quantum-inspired technologies are set to significantly impact the energy sector, enhancing optimisation and simulation processes to enable studies that cannot be performed today. At Siemens Energy, our studies have shown potential to address complex challenges in grid management, power distribution, and scarce resource allocation. One of the important examples of where quantum computing holds promise is in the field of optimisation, where physical systems are used to find low energy (i.e. high quality) states (i.e. solutions) to systems that could support many configurations (i.e. combinatorial optimisation problems). The integration of quantum computing into the energy industry promises to deliver improvements in several fields, including cost reduction, increased system reliability, and importantly to be able to make better use of existing resources. Currently in the early stages of research and development, Siemens Energy’s work is showing that combinatorial optimisation problems are all around us in the energy sector. Practical quantum-inspired and hybrid approaches can be used today to solve some of these problems and bring benefits in areas which would have previously not employed optimisation traditionally. This gives us the opportunity and tools to attempt new and innovative approaches to decarbonisation and attempt previously insoluble challenges in the industry.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".