AI Article Synopsis
- This study introduces a machine learning-based solver for accurately simulating dissipative quantum dynamics in open quantum systems.
- The proposed neural quantum propagator model leverages neural networks to efficiently evolve quantum states without the need for lengthy iterations.
- The model's effectiveness is showcased through simulations of population dynamics and spectra for the Fenna-Matthews-Olson complex.
Article Abstract
The accurate solution of dissipative quantum dynamics plays an important role in the simulation of open quantum systems. Here, we propose a machine learning-based universal solver for the hierarchical equations of motion, one of the most widely used approaches which takes into account non-Markovian effects and nonperturbative system-environment interactions in a numerically exact manner. We develop a neural quantum propagator model by utilizing the neural network architecture, which avoids time-consuming iterations and can be used to evolve any initial quantum state for arbitrarily long times. To demonstrate the efficacy of our model, we apply it to the simulation of population dynamics and linear and two-dimensional spectra of the Fenna-Matthews-Olson complex.
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| http://dx.doi.org/10.1039/d4cp03736g | DOI Listing |
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