Reinforcement learning for path integrals in quantum statistical physics (opens in new tab)

arXiv:2602.16176v1 Announce Type: new Abstract: Machine learning is rapidly finding its way into the field of computational quantum physics. One of the most popular and widely studied approaches in this direction is to use neural networks to model quantum states (NQS) in the Hamiltonian formulation of quantum mechanics. However, an alternative angle of attack to leverage machine learning in physics is through the path integral formulation, which has so far received far more limited attention...