Efficient Simulation of Correlated Dynamics in Quantum-Dot Cellular Automata (QCA)
Why this work is in the frame
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Bibliographic record
Abstract
Many simulations of quantum-dot cellular automata (QCA) rely upon the so-called intercellular Hartree approximation (ICHA), which neglects the possibility of entanglement between cells. While the ICHA is useful for solving many QCA circuits due to its relative simplicity and computational efficiency, its many shortcomings make it prohibitive in accurately modeling the dynamics of large systems of QCA cells. On the other hand, solving a full Hamiltonian for each circuit, while more accurate, becomes computationally intractable as the number of cells increases. This paper aims to find an intermediate solution that exists somewhere in the solution space spanned by the ICHA and the full Hamiltonian. The development of such a solution promises to yield significant results within the QCA research community.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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