Schur-complement truncations of HEOM for finite-dimensional systems converge spectrally to the full equations and are free of spectral pollution when the exact HEOM is stable.
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A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
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On truncations of hierarchical equations of motion for finite-dimensional systems
Schur-complement truncations of HEOM for finite-dimensional systems converge spectrally to the full equations and are free of spectral pollution when the exact HEOM is stable.
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Non-markovian neural quantum propagator and its application to the simulation of ultrafast nonlinear spectra
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.