Self-Adaptive Quantum and Classical Software Systems
Bibliographic record
Abstract
Quantum computing has the potential to revolutionize various industries by solving complex problems beyond the capabilities of classical systems. However, the practical realization of these advancements depends on robust hybrid software capable of adapting to highly dynamic execution environments. Fluctuating resource availability, quantum noise, and hardware constraints in distributed systems present significant challenges, making self-adaptation mechanisms essential for optimizing performance, ensuring reliability, and maintaining scalability. Traditional static approaches to software design are insufficient in such unpredictable settings, requiring runtime adaptation strategies that continuously monitor and adjust system behavior. Frameworks such as autonomic computing models and dynamic software architectures provide viable solutions for managing uncertainty and improving computational efficiency. By integrating self-adaptive capabilities, hybrid quantum software can dynamically respond to execution constraints, enhance system resilience, and support real-world applications, ensuring that quantum computing can effectively complement classical computing to address the growing demands of high-performance computation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".