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Quantum targeted energy transfer through machine learning tools.

I Andronis1, G Arapantonis1,2, G D Barmparis1

  • 1Department of Physics, University of Crete, Heraklion 70013, Greece.

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Summary
This summary is machine-generated.

This study explores quantum targeted energy transfer using machine learning to find efficient boson transfer paths in crystal systems. The method identifies resonant pathways for simultaneous boson transfer, applicable to larger systems.

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Area of Science:

  • Quantum physics
  • Condensed matter physics
  • Computational chemistry

Background:

  • Quantum targeted energy transfer involves moving bosons between crystal sites.
  • Classical targeted energy transfer uses nonlinear resonance configurations.

Purpose of the Study:

  • Investigate the selectivity and efficiency of quantum transfer.
  • Explore boson transfer in dimer and trimer systems.
  • Develop a machine learning-based computational method.

Main Methods:

  • Utilized a computational method based on machine learning algorithms.
  • Applied the method to dimer and trimer systems.
  • Investigated nonlinear resonance configurations for quantum transfer.

Main Results:

  • Identified resonant quantum transfer paths.
  • Demonstrated boson transfer in unison.
  • Achieved high selectivity and efficiency in quantum transfer.

Conclusions:

  • The machine learning method effectively identifies optimal quantum transfer pathways.
  • The approach is scalable to larger lattice systems.
  • Confirms the utility of nonlinear resonances in quantum energy transfer.