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Convolutional Neural Networks for Long Time Dissipative Quantum Dynamics.

Luis E Herrera Rodríguez1,2,3, Alexei A Kananenka3

  • 1Departamento de Física, Universidad Nacional de Colombia, Carrera 30 No. 45-03, Bogotá D.C., Colombia.

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|March 5, 2021
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Deep artificial neural networks accurately predict long-time dynamics of open quantum systems. This computational approach reduces resource needs for simulating quantum phenomena, like those in photosynthesis.

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Area of Science:

  • Quantum Physics
  • Computational Science
  • Artificial Intelligence

Background:

  • Simulating open quantum systems demands significant computational power.
  • Understanding long-time dynamics is crucial for various quantum applications.

Purpose of the Study:

  • To develop an efficient and accurate method for predicting long-time dynamics of open quantum systems.
  • To leverage deep artificial neural networks for quantum system simulations.

Main Methods:

  • A deep artificial neural network utilizing convolutional layers was developed.
  • The model was trained on short-time evolution data of quantum systems.
  • The network predicts system dynamics across different regimes, including coherent motion and relaxation.

Main Results:

  • The neural network accurately predicts long-time dynamics of open quantum systems.
  • The model demonstrates efficiency across weakly damped coherent motion and incoherent relaxation regimes.
  • The approach is effective even for initial conditions not used during training.

Conclusions:

  • Deep artificial neural networks offer a powerful and resource-efficient tool for simulating open quantum systems.
  • The developed model is applicable to studying phenomena like quantum coherence in light-harvesting complexes.
  • This method promises to be valuable for future research in open quantum systems.