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Record W2018718678 · doi:10.1109/tnano.2014.2300494

Efficient Simulation of Correlated Dynamics in Quantum-Dot Cellular Automata (QCA)

2014· article· en· W2018718678 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Nanotechnology · 2014
Typearticle
Languageen
FieldComputer Science
TopicQuantum-Dot Cellular Automata
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQuantum cellular automatonQuantum dot cellular automatonCellular automatonQuantum entanglementHamiltonian (control theory)Electronic circuitComputer scienceQuantum dotHartreeQuantumQuantum computerStatistical physicsMathematicsPhysicsQuantum mechanicsAlgorithmMathematical optimization

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.226
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it