Quantum computing for solving Bayesian networks of bridges – method and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of quantum computing as an efficient alternative to classical computing is rapidly evolving in diverse engineering applications to solve complex numerical problems. As its application becomes increasingly prominent in the future, its use should be complemented by validation and practical recommendations. This paper provides practical recommendations regarding the accuracy and efficiency of quantum computing for solving diverse types of Bayesian networks for bridge evaluation and maintenance, including basic and fuzzy-based networks. The methodology for solving the considered networks, development of quantum circuits, and accuracy of the results (quantum computing versus simulator and classical computing) were examined. Research findings indicate the feasibility of quantum computing for solving small-scale bridge Bayesian networks (error of 6% approximately), whereas the accuracy of solutions for solving complex networks using available open-access quantum computers is compromised owing to the limited number of available attempts and the compound effect of quantum error (error up to 37% approximately). Practical recommendations were provided to practitioners and future research needs are identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it