Cross-Platform Autonomous Control of Minimal Kitaev Chains

<p>This folder contains the raw data and code used to generate the plots for the paper <em>Cross-Platform Autonomous Control of Minimal Kitaev Chains</em>.</p> <p>To run the Jupyter notebook, install Anaconda and execute:</p> <blockquote> <p><em>conda env create -f&nbsp;</em>environment.yml</p> </blockquote> <p>followed by:</p> <blockquote> <p><em>conda activate KitaevML</em></p> </blockquote> <p>Finally,</p> <blockquote> <p><em>jupyter lab</em></p> </blockquote> <p>to launch the notebook.</p> <p>Raw data are stored in netCDF (.nc) format, or as full QCoDeS databases (.db). Logs of autonomous tuning runs are saved as log files (.txt). The experimental datasets are exported by the data acquisition package QCoDeS and can be read as an xarray Dataset. The ipython notebook <em>Data labelling notebook.ipynb </em>contains the code for labelling the 2DEG and nanowire datasets. The ipython notebook <em>Zenodo Paper Figures.ipynb</em> contains all the code for generating the paper figures.&nbsp;</p> <p>The machine learning model and automated tuning algorithm can be publicly accessed here: https://gitlab.com/QMAI/papers/crossplatformkitaev.<br>This repo contains a pip-installable module ``PMMSGD` that can be used to predict (Delta-t)/(Delta+t) using:</p> <pre><code>pred = PMMSGD.get_Delta_minus_t(dbx, 'CNN_weighted_ratio_theory', ['G_LL*G_RR'])</code></pre> <p>which uses the neural network that was exclusively trained on theory data</p> <p>For the automated tuning algorithm we used this predictor:</p> <pre><code>pred = PMMSGD.get_Delta_minus_t(dbx, 'CNN_weighted_ratio', ['G_LL*G_RR'])</code></pre> <p><br>which uses the neural network that was additionally trained on data from the 2DEG device.</p>