PINN-DQME uses time-encoded neural networks to simulate open quantum system evolution with high accuracy at high temperatures but accumulates errors in strongly non-Markovian low-temperature regimes.
Artificial-intelligence-based surrogate solution of dissi- pative quantum dynamics: physics-informed reconstruc- tion of the universal propagator
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Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network
PINN-DQME uses time-encoded neural networks to simulate open quantum system evolution with high accuracy at high temperatures but accumulates errors in strongly non-Markovian low-temperature regimes.