Low-Energy Eigenspectrum Decomposition (LEED) of Quantum-Dot Cellular Automata Networks
Bibliographic record
Abstract
The design and understanding of quantum-dot cellular automata (QCA) networks has been largely influenced by limitations in the approximation methods used in common design tools. In some cases, such limitations have led to unrealistic selections of clock zones which are not feasible for nanoscale QCA implementations given current fabrication constraints on clocking electrodes. A better understanding of the behaviour of larger QCA networks of perhaps tens to hundreds of QCA devices is needed. One approach is by investigating the low energy spectrum; however, diagonalization of the system Hamiltonian even in the 2-state approximation is impractical beyond 20 or so devices. In this work, we present a methodology for understanding the spectrum of the full network in terms of contributions from components of the network. We show that important features of the low energy spectrum can be attributed to specific critical components, and present one scheme for decomposing the network into these components. In addition, we address the question of computing the low energy spectrum of large QCA networks. A method based on basis reduction which naturally emerges from the component decomposition is successfully applied to a 49 cell XOR gate with results compared against a density matrix renormalization group implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".