A single wave function for multiple systems: Neural-Network Quantum States as Foundation Models

Neural-Network Quantum States are becoming one of the most powerful method to simulate quantum many-body systems. We present a simple framework in which a single architecture is trained to approximate simultaneously the ground state of multiple systems. Performing a single simulation, we get an accurate description of the entire phase diagram of quantum spin models. This approach yields comparable accuracy to training separate networks from scratch at each point on the phase diagram, yet demands only the computational cost of a single simulation. Moreover, our findings demonstrate that the network exhibits remarkable generalization capabilities across unseen models.