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Trapped Ion Quantum Computing Quantum Machine Learning

Spectral Density Classification For Environment Spectroscopy

arXiv
Authors: Jessica Barr, Giorgio Zicari, Alessandro Ferraro, Mauro Paternostro

Year

2023

Paper ID

56192

Status

Preprint

Abstract Read

~2 min

Abstract Words

103

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • Spectral densities encode the relevant information characterising the system-environment interaction in an open-quantum system problem.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2308.00831 [2] arXiv https://arxiv.org/abs/2308.00831 [3] Publisher https://arxiv.org/abs/2308.00831

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