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This study introduces a deep learning method to Feynman's path integral formulation for strong-field physics. This approach efficiently predicts quantum wave function evolution, overcoming limitations of traditional methods in attosecond science.

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Area of Science:

  • Quantum physics
  • Strong-field physics
  • Attosecond science

Background:

  • Feynman's path integral sums all paths to describe quantum wave functions.
  • Characterizing infinite paths is computationally challenging, limiting applications in strong-field and attosecond physics.

Purpose of the Study:

  • To develop an efficient deep learning approach for Feynman's path integral in strong-field physics.
  • To overcome computational limitations of traditional methods for quantum wave function evolution.

Main Methods:

  • A novel deep-learning-based Feynman formulation utilizing a preclassification scheme.
  • Direct prediction of final quantum states from initial conditions, avoiding brute-force path summation.

Main Results:

  • The proposed method successfully predicts quantum wave function evolution in strong-field scenarios.
  • Demonstrated ability to tackle complex problems intractable for existing strong-field techniques.

Conclusions:

  • Deep learning integrated with Feynman's path integral offers a powerful new tool for strong-field and attosecond science.
  • This approach bridges quantum mechanics and classical views, enabling new physics exploration and understanding quantum-classical correspondence.