Spectral Density Classification For Environment Spectroscopy
- URL: http://arxiv.org/abs/2308.00831v2
- Date: Tue, 12 Mar 2024 11:30:36 GMT
- Title: Spectral Density Classification For Environment Spectroscopy
- Authors: Jessica Barr, Giorgio Zicari, Alessandro Ferraro, Mauro Paternostro
- Abstract summary: We leverage the potential of machine learning techniques to reconstruct the features of the environment.
For relevant examples of spin-boson models, we can classify with high accuracy the Ohmicity parameter of the environment as either Ohmic, sub-Ohmic or super-Ohmic.
- Score: 44.99833362998488
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Spectral densities encode the relevant information characterising the
system-environment interaction in an open-quantum system problem. Such
information is key to determining the system's dynamics. In this work, we
leverage the potential of machine learning techniques to reconstruct the
features of the environment. Specifically, we show that the time evolution of a
system observable can be used by an artificial neural network to infer the main
features of the spectral density. In particular, for relevant examples of
spin-boson models, we can classify with high accuracy the Ohmicity parameter of
the environment as either Ohmic, sub-Ohmic or super-Ohmic, thereby
distinguishing between different forms of dissipation.
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