Benchmarking quantum tomography completeness and fidelity with machine learning
We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out st...
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| Principais autores: | , , , , , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
IOP Publishing
2021-01-01
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| Series: | New Journal of Physics |
| Assuntos: | |
| Acceso en liña: | https://doi.org/10.1088/1367-2630/ac1fcb |
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