Distribution of non-Gaussian states in a deployed telecommunication fiber channel

Raw and partially processed data behind the results presented in C. A. Breum, X. Guo, M. V. Larsen, S. Miki, H. Terai, U. L. Andersen and J. S. Neergaard-Nielsen, "Distribution of non-Gaussian states in a deployed telecommunication fiber channel", arXiv:2509.18080.The single .hdf5 file contains all data in the following structure:distributed_nongaussian_states.hdf5<br>├── raw traces 307<br>│ ├── signal<br>│ └── vacuum<br>├── raw traces 340<br>│ ├── signal<br>│ └── vacuum<br>├── extracted quadrature values 307<br>│ ├── kitten<br>│ ├── squeezed<br>│ ├── vacuum<br>│ ├── modefunction kitten<br>│ └── modefunction squeezed<br>└── extracted quadrature values 340<br> ├── kitten<br> ├── squeezed<br> ├── vacuum<br> ├── modefunction kitten<br> └── modefunction squeezed<br>In Python, all datasets can be extracted into a single dictionary <code>data</code> using the h5py package and the following code:<pre>import h5py<br><br>def recursive_extract(name, obj):<br> if isinstance(obj, h5py.Dataset):<br> return obj[()]<br> elif isinstance(obj, h5py.Group):<br> return {key: recursive_extract(key, obj[key]) for key in obj}<br> else:<br> return None<br><br>with h5py.File('distributed_nongaussian_states.hdf5', 'r') as f:<br> data = {key: recursive_extract(key, f[key]) for key in f}</pre>