Sample-efficient estimation of entanglement entropy through supervised learning
We explore a supervised machine-learning approach to estimate the entanglement entropy of multiqubit systems from few experimental samples. We put a particular focus on estimating both aleatoric and epistemic uncertainty of the network's estimate and benchmark against the best-known conventiona...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
January 2024
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| In: |
Physical review
Year: 2024, Jahrgang: 109, Heft: 1, Pages: 1-6 |
| ISSN: | 2469-9934 |
| DOI: | 10.1103/PhysRevA.109.012403 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1103/PhysRevA.109.012403 Verlag, lizenzpflichtig, Volltext: https://link.aps.org/doi/10.1103/PhysRevA.109.012403 
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| Verfasserangaben: | Maximilian Rieger, Moritz Reh, and Martin Gärttner |
MARC
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| 520 | |a We explore a supervised machine-learning approach to estimate the entanglement entropy of multiqubit systems from few experimental samples. We put a particular focus on estimating both aleatoric and epistemic uncertainty of the network's estimate and benchmark against the best-known conventional estimation algorithms. For states that are contained in the training distribution, we observe convergence in a regime of sample sizes in which the baseline method fails to give correct estimates, while extrapolation only seems possible for regions close to the training regime. As a further application of our method, highly relevant for quantum simulation experiments, we estimate the quantum mutual information for nonunitary evolution by training our model on different noise strengths. | ||
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