Nature Physics, Published online: 01 July 2024; doi:10.1038/s41567-024-02566-1
An optimization algorithm reduces the cost of training large-scale neural quantum states. This leads to accurate computations of the ground states of frustrated magnets and provides evidence of gapless quantum-spin-liquid phases.
