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arXiv · 2002.11699

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Xiwang Liu·Guojun Zhang·Jie Li·Guangluo Shi·Mingyang Zhou·Boqiang Huang·Yajuan Tang·Xiaohong Song·Weifeng Yang
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Abstract

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence.

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Xiwang Liu, Guojun Zhang, Jie Li, Guangluo Shi, Mingyang Zhou, Boqiang Huang, Yajuan Tang, Xiaohong Song, Weifeng Yang. 2020-02-29. Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics. https://doi.org/10.1103/physrevlett.124.113202

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