Learning to detect optical nonclassicality
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| Formato: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901963579326464 |
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| author | Jung, Martina Gärttner, Martin |
| author_facet | Jung, Martina Gärttner, Martin |
| contents | <p>This repository includes the datasets used for training and testing as well as the saved parameters.</p> <p><strong>datasets</strong>:</p> <ul> <li><em>ds12_fixed</em>: Single-mode dataset, simulated with a perfect photon-number-resolving (PNR) detector with a cutoff at $n=29$ photons</li> <li><em>ds14_PNR:</em> Single-mode dataset, simulated with the experimentally reconstructed POVM of a detector with finite photon-number resolution. Coherent states were measured experimentally.</li> <li><em>ds15</em>_<em>8Bin</em>: Single-mode dataset, simulated with the experimentally reconstructed POVM of a time-bin multiplexing scheme with $N=8$ bins in total. Coherent states were measured experimentally.</li> <li><em>ds16_6modes</em>: 6-mode dataset, simulated with the experimentally reconstructed POVM of a detector with finite photon-number resolution.</li> <li>code used for data simulation</li> </ul> <p>Each folder contains a file <em>state_configs.txt</em> that lists the amplitudes of the simulated states.</p> <p><strong>saved_params</strong>:</p> <ul> <li>contains the parameters after training on the different datasets</li> <li>the subfolders <em>1el</em> and <em>2el </em>represent the training with one and two encoding layers</li> <li>the subfolders <em>1kshots</em>, <em>10kshots</em> etc. represent the number of samples per state</li> <li>the subfolder <em>dense_decoder</em> represents the training with a model whose algebraic decoder is replaced with a dense feed-forward neural network</li> </ul> <p><strong>code:</strong></p> <ul> <li><em>PolynomialRegression</em> <ul> <li><em>EncoderOutputsAndModelsPrediction</em>: tuples of encoder outputs and predicted labels that are used as input for the polynomial regression</li> <li><em>ResultsPolyRegression:</em> results of the polynomial regression</li> <li>code used to perform the polynomial regression</li> </ul> </li> <li><em>tests</em>: test functions</li> <li>code used for set-up and training of the algebraic classifier</li> <li>.yaml file that allows to create the environment for the code</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18647273 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Learning to detect optical nonclassicality Jung, Martina Gärttner, Martin <p>This repository includes the datasets used for training and testing as well as the saved parameters.</p> <p><strong>datasets</strong>:</p> <ul> <li><em>ds12_fixed</em>: Single-mode dataset, simulated with a perfect photon-number-resolving (PNR) detector with a cutoff at $n=29$ photons</li> <li><em>ds14_PNR:</em> Single-mode dataset, simulated with the experimentally reconstructed POVM of a detector with finite photon-number resolution. Coherent states were measured experimentally.</li> <li><em>ds15</em>_<em>8Bin</em>: Single-mode dataset, simulated with the experimentally reconstructed POVM of a time-bin multiplexing scheme with $N=8$ bins in total. Coherent states were measured experimentally.</li> <li><em>ds16_6modes</em>: 6-mode dataset, simulated with the experimentally reconstructed POVM of a detector with finite photon-number resolution.</li> <li>code used for data simulation</li> </ul> <p>Each folder contains a file <em>state_configs.txt</em> that lists the amplitudes of the simulated states.</p> <p><strong>saved_params</strong>:</p> <ul> <li>contains the parameters after training on the different datasets</li> <li>the subfolders <em>1el</em> and <em>2el </em>represent the training with one and two encoding layers</li> <li>the subfolders <em>1kshots</em>, <em>10kshots</em> etc. represent the number of samples per state</li> <li>the subfolder <em>dense_decoder</em> represents the training with a model whose algebraic decoder is replaced with a dense feed-forward neural network</li> </ul> <p><strong>code:</strong></p> <ul> <li><em>PolynomialRegression</em> <ul> <li><em>EncoderOutputsAndModelsPrediction</em>: tuples of encoder outputs and predicted labels that are used as input for the polynomial regression</li> <li><em>ResultsPolyRegression:</em> results of the polynomial regression</li> <li>code used to perform the polynomial regression</li> </ul> </li> <li><em>tests</em>: test functions</li> <li>code used for set-up and training of the algebraic classifier</li> <li>.yaml file that allows to create the environment for the code</li> </ul> |
| title | Learning to detect optical nonclassicality |
| url | https://doi.org/10.5281/zenodo.18647273 |