Atomistic first-principles modeling of single donor spin-qubit
Bibliographic record
Abstract
Using an impurity atom in crystal silicon as a spin-1/2 qubit has been made experimentally possible recently where the impurity atom acts as a quantum dot (QD). Quantum transport in and out of such a donor QD occurs in the sequential tunneling regime where a physical quantity of importance is the charging (addition) energy, which measures the energy necessary for adding an electron into the donor QD. In this work, we present a first-principles method to quantitatively predict the addition energy of the donor QD. Using density functional theory (DFT), we determine the impurity states that serve as the basis set for subsequent exact diagonalization calculation of the many-body states and energies of the donor QD. Due to the large effective Bohr radius of the conduction electrons in Si, very large supercells containing more than 10 000 atoms must be used to obtain accurate results. For the donor QD of a phosphorus impurity in bulk Si, the combined DFT and exact diagonalization predicts the first addition energy to be 53 meV, in good agreement with the corresponding experimental value. For the donor QD of an arsenic impurity in Si, the first addition energy is predicted to be 44.2 meV. The calculated many-body wave functions provide a vivid electronic picture of the donor QD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".