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

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.

References in corpus (18)

• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Learning to Plan Chemical Syntheses
• Machine learning phases of matter
• Many-body quantum state tomography with neural networks
• Learning phase transitions by confusion
• Deep-STORM: super-resolution single-molecule microscopy by deep learning
• Discovering physical concepts with neural networks
• Low-Energy Structures in Strong Field Ionization Revealed by Quantum Orbits
• Machine Learning Topological Invariants with Neural Networks
• Matching matched filtering with deep networks in gravitational-wave astronomy
• Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model
• Semiclassical two-step model for strong-field ionization
• Machine learning meets quantum physics
• Quantum Machine Learning for Electronic Structure Calculations
• Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
• Photoelectron holographic interferometry to probe the longitudinal momentum offset at the tunnel exit
• Momentum mapping of continuum electron wave packet interference
• Attosecond Interference Induced by Coulomb-Field-Driven Transverse Backward-Scattering Electron Wave-Packets

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