Algorithms for Embedding Quantum-Dot Cellular Automata Networks onto a\n Quantum Annealing Processor
Bibliographic record
Abstract
Advancements in computing based on qubit networks, and in particular the\nflux-qubit processor architecture developed by D-Wave System's Inc., have\nenabled the physical simulation of quantum-dot cellular automata (QCA) networks\nbeyond the limit of classical methods. However, the embedding of QCA networks\nonto the available processor architecture is a key challenge in preparing such\nsimulations. In this work, two approaches to embedding QCA circuits are\ncharacterized: a dense placement algorithm that uses a routing method based on\nnegotiated congestion; and a heuristic method implemented in D-Wave's Solver\nAPI package. A set of benchmark QCA networks is used to characterise the\nalgorithms and a stochastic circuit generator is employed to investigate the\nperformance for different processor sizes and active flux-qubit yields.\n
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".