Data and code associated to the paper "Comment on 'Beyond-classical computation in quantum simulation' "

This Zenodo entry contains the data, scripts and source code needed toreproduce the figure of the publication**"Comment on 'Beyond-classical computation in quantum simulation'"**(W. Krinitsin, N. Alert, M. Rizzi, M. Schmitt): <https://doi.org/10.48550/arXiv.2607.08811> The comment refers to A. D. King *et al.*, "Beyond-classical computation inquantum simulation", Science 388, 199-204 (2025). The reproduction chain has four steps, all fully contained in this entry: 1. `data/nqs_sampling_runs/*/run.py` samples ZZ correlations from the time-evolved NQS wave functions (variational parameters included). Requires jVMC/JAX and a GPU; the sampled data ship with this entry, so this step only needs to be run to regenerate them from scratch.2. `data/compute_plot_data.py` (Python) computes the observables of the figure from the sampled data and the MPS/QPU reference correlations, and writes the plain-text CSV files in `data/`.3. `plots.ipynb` (Julia) reads those CSVs and produces the figure into `figures/`.4. Label positions were then later adjusted in Inkscape. The derived CSVs ship with this entry, so step 3 can be run directly. Allpaths are relative to this folder; nothing outside it is needed. ## Top-level layout ```zenodo/├── README.md├── plots.ipynb Julia notebook that produces the figure├── Project.toml Julia environment of the notebook├── Manifest.toml (activated by the notebook itself)├── figures/ Output of the notebook│ (figure1_reference.pdf = figure as published)└── data/ Raw data, sampling runs, observable computation ├── requirements.txt Python packages for compute_plot_data.py └── nqs_sampling_runs/ └── requirements.txt Python environment of the sampling runs``` `data/` contains its own `README.md` describing the sampling runs (encaplayout), the variational parameters, the reference data and how each CSVfeeds the individual panels of the figure.