Dataset and scripts used in: g-tensor Optimization in Ge/SiGe Quantum Dots

This repository contains the data and scripts used in the manuscript: “g-tensor Optimization in Ge/SiGe Quantum Dots” Aram Shojaei, Edmondo Valvo, Maximilian Rimbach-Russ, Eliska Greplova, Ana Silva The code implements the numerical workflow used to: 1. model vertical heavy-hole (HH) and light-hole (LH) subbands in a strained Ge/SiGe quantum well with a segmented Si profile,2. calculate the effective in-plane Zeeman response, in particular g_xx, using the perturbative Schrieffer–Wolff / Luttinger–Kohn framework,3. optimize the Si profile using the CMA-ES algorithm. The simulation scripts are stored in `scr/`, while processed results and plotting data are stored in `scr/results/`. The notebook used to generate the manuscript figures is located in `plotting/`. Repository structure `scr/`Directory containing the main simulation and optimization scripts. `main.py`Program used to run the CMA-ES optimization loop for the segmented Si profile and evaluate the corresponding g-tensor response. Input: Simulation parameters and model definitions implemented in the source code.Output: Raw optimization results saved in `scr/results/` as generation/population files. `scr/results/`Directory containing the outputs of optimization runs and post-processing analysis. `*.json`Files containing the raw CMA-ES optimization generations and populations. `post_processed_data/`Directory containing aggregated datasets used for analysis and plotting.Output: Processed `.csv` and `.npz` files used for plotting and interpretation of the optimization results. `plotting/`Directory containing the plotting workflow for the manuscript figures. `Plotting_notebook.ipynb`Notebook used to generate the manuscript plots from the processed optimization results.Input: Processed datasetes in`scr/results/post_processed_data/` Output: Figures for analysis and manuscript preparation. `figures/`Directory containing exported figures generated from the plotting notebook. Installation The project dependencies are listed in: `requirements.txt` Install them with: pip install -r requirements.txt