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Photoinduced nonequilibrium states can provide new insight into dynamicalproperties of strongly correlated electron systems. One of the typical andextensively studied systems is the half-filled one-dimensional extended Hubbardmodel (1DEHM). Here, we propose that the supervised machine learning (ML) canprovide useful information for characterizing photoexcited states in 1DEHM.Using entanglement spectra as a training dataset, we construct neural network.Judging from the trained network, we find that bond-spin-density wave (BSDW)order can be enhanced in photoexcited states if the frequency of a drivingpulse nearly resonates with gap. We separately calculate the time evolution oflocal and non-local order parameters and confirm that the correlation functionsof BSDW are actually enhanced by photoexcitation as predicted by ML. Thesuccessful prediction of BSDW demonstrates the advantage of ML to assistcharacterizing photoexcited quantum states.

Characterization of photoexcited states in the half-filled one-dimensional extended Hubbard model assisted by machine learning