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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The virtual-excitation protocol outperforms the trimer-model approximation for entanglement distribution in spin chains in speed, fidelity, and noise resilience.
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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.
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Spin Chains for Quantum Information Processing
The virtual-excitation protocol outperforms the trimer-model approximation for entanglement distribution in spin chains in speed, fidelity, and noise resilience.