Learning Hamiltonians for solid-state quantum simulators
This paper introduces a physics-informed neural network framework that enables the unsupervised learning of effective Hamiltonian parameters directly from experimental transport data in solid-state quantum simulators, utilizing an autoencoder architecture with a physics-decoder to ensure physically meaningful representations, robust generalization, and noise resilience.