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Simulating the Photon Statistics of Gaussian States Employing Automatic Differentiation from PyTorch

Fitzke, Erik ; Niederschuh, Florian ; Walther, Thomas (2023)
Simulating the Photon Statistics of Gaussian States Employing Automatic Differentiation from PyTorch.
doi: 10.26083/tuprints-00023061
Report, Primary publication, Publisher's Version

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Technical_Report_Photon_Statistics.pdf
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Technical_Report_Photon_Statistics.py
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Item Type: Report
Type of entry: Primary publication
Title: Simulating the Photon Statistics of Gaussian States Employing Automatic Differentiation from PyTorch
Language: English
Date: 2023
Place of Publication: Darmstadt
Collation: 13 Seiten
DOI: 10.26083/tuprints-00023061
Abstract:

Many common photonic states are so-called Gaussian states. In a recent manuscript, we have shown how the photon statistics of such states can be obtained by constructing and differentiating generating functions. In this technical report, we demonstrate the straightforward application of the framework PyTorch to compute the required multivariate higher-order derivatives by automatic differentiation. Its implementation requires only a few lines of Python code corroborating the strength of our approach based on generating functions for the computation of photon statistics.

Uncontrolled Keywords: PyTorch, Gaussian boson sampling, Gaussian states, probability generating function, photon statistics, quantum key distribution, quantum simulation, automatic differentiation
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-230615
Classification DDC: 500 Science and mathematics > 530 Physics
Divisions: 05 Department of Physics > Institute of Applied Physics > Laser und Quantenoptik
Date Deposited: 09 Jan 2023 13:11
Last Modified: 10 Jan 2023 09:09
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23061
PPN: 50345785X
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