SymGF: a symbolic tool for quantum transport analysis and its application to a double quantum dot system
Bibliographic record
Abstract
We report the development and an application of a symbolic tool, called SymGF, for analytical derivations of quantum transport properties using the Keldysh nonequilibrium Green's function (NEGF) formalism. The inputs to SymGF are the device Hamiltonian in the second quantized form, the commutation relation of the operators and the truncation rules of the correlators. The outputs of SymGF are the desired NEGF that appear in the transport formula, in terms of the unperturbed Green's function of the device scattering region and its coupling to the device electrodes. For complicated transport analysis involving strong interactions and correlations, SymGF provides significant assistance in analytical derivations. Using this tool, we investigate coherent quantum transport in a double quantum dot system where strong on-site interaction exists in the side-coupled quantum dot. Results obtained by the higher-order approximation and Hartree-Fock approximation are compared. The higher-order approximation reveals Kondo resonance features in the density of states and conductances. Results are compared both qualitatively and quantitatively to the experimental data reported in the literature.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".