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Machine Learning Non-Markovian Quantum Dynamics.

I A Luchnikov1,2, S V Vintskevich1, D A Grigoriev1

  • 1Moscow Institute of Physics and Technology, Institutskii Pereulok 9, Dolgoprudny, Moscow Region 141700, Russia.

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This study introduces a machine learning method to uncover unknown quantum environments by analyzing measurement patterns. It enables efficient control and manipulation of quantum systems by learning environmental information.

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Area of Science:

  • Quantum Physics
  • Machine Learning
  • Quantum Information Science

Background:

  • Quantum measurements exhibit patterns in open quantum systems due to system-environment interactions.
  • These patterns contain crucial information about relaxation rates and non-Markovian memory effects.
  • Extracting environmental information typically requires complex process tomography.

Purpose of the Study:

  • To develop a novel method for extracting information about unknown quantum environments.
  • To avoid the need for process tomography by using projective single-shot measurements.
  • To enable efficient control and manipulation of quantum systems.

Main Methods:

  • Embedding non-Markovian system dynamics into a Markovian dynamics framework.
  • Utilizing an effective reservoir of finite dimension for the embedding.
  • Learning the Markovian embedding generator via maximum likelihood estimation.

Main Results:

  • Successfully extracted information about unknown quantum environments from measurement data.
  • Verified the method's accuracy against an exactly solvable non-Markovian dynamics model.
  • Demonstrated the algorithm's capability to learn environmental characteristics.

Conclusions:

  • The developed machine learning approach provides an efficient way to characterize quantum environments.
  • This method bypasses the need for traditional, resource-intensive process tomography.
  • Enables advanced control and manipulation of quantum systems by understanding their environments.