Fourier-Flow model generating Feynman paths

Understand

As an alternative but unified and more fundamental description for quantum physics, Feynman path integrals generalize the classical action principle to a probabilistic perspective, under which the physical observables' estimation translates into a weighted sum over all possible paths.

• The underlying difficulty is to tackle the whole path manifold from finite samples that can effectively represent the Feynman propagator dictated probability distribution.
• Modern generative models in machine learning can handle learning and representing probability distribution with high computational efficiency.
• In this study, we propose a Fourier-flow generative model to simulate the Feynman propagator and generate paths for quantum systems.