Algorithms for Embedding Quantum-Dot Cellular Automata Networks onto a\n Quantum Annealing Processor
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.001 | 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 it